Clin Pharmacol Ther. 2025 May 5. doi: 10.1002/cpt.3705. Online ahead of print.

ABSTRACT

The use of elexacaftor/tezacaftor/ivacaftor (ETI) has been associated with increased fertility in women with cystic fibrosis (CF) and is increasingly used during pregnancy to support both maternal and fetal health. However, little is known about the pharmacokinetics (PK) of ETI during pregnancy, which is crucial for optimizing its efficacy and safety. This study aimed to predict the PK of ETI during pregnancy and to determine the maternal dose required to achieve therapeutic concentrations in both the maternal and fetus. The pregnancy physiological-based pharmacokinetic (PBPK) model within the Simcyp Simulator was used to predict the maternal and feto-placental exposure of ETI. Placental kinetics were parameterized using permeability parameters determined from the physicochemical properties of these compounds. The model closely predicted the observed data, with the observed ETI maternal plasma concentrations, cord concentrations, and infant plasma concentrations mostly falling within the range of predicted 5th to 95th percentiles. Steady-state simulations up to gestational week 40 predicted a continuous decline in ETI concentrations, with the AUC declining to 32.4-37.5% of baseline levels by week 40. However, the 5th percentile of trough concentrations for ETI consistently remained above the efficacy thresholds, both in mother and fetus. Therefore, it appears reasonable to maintain standard dosing regimen during pregnancy, complemented by careful monitoring. A clinical trial, such as the ongoing Maternal and Fetal Outcomes in the Era of Modulators (MAYFLOWERS) study, is required to further confirm the efficacy and safety of ETI in this population.

PMID:40323155 | DOI:10.1002/cpt.3705

J Med Internet Res. 2025 May 5;27:e69906. doi: 10.2196/69906.

ABSTRACT

BACKGROUND: Over the past few years, radiomics for the detection of intrahepatic cholangiocarcinoma (ICC) has been extensively studied. However, systematic evidence is lacking in the use of radiomics in this domain, which hinders its further development.

OBJECTIVE: To address this gap, our study delved into the status quo and application value of radiomics in ICC and aimed to offer evidence-based support to promote its systematic application in this field.

METHODS: PubMed, Web of Science, Cochrane Library, and Embase were comprehensively retrieved to determine relevant original studies. The study quality was appraised through the Radiomics Quality Score. In addition, subgroup analyses were undertaken according to datasets (training and validation sets), imaging sources, and model types.

RESULTS: Fifty-eight studies encompassing 12,903 patients were eligible, with an average Radiomics Quality Score of 9.21. Radiomics-based machine learning (ML) was mainly used to diagnose ICC (n=30), microvascular invasion (n=8), gene mutations (n=5), perineural invasion (PNI; n=2), lymph node (LN) positivity (n=2), and tertiary lymphoid structures (TLSs; n=2), and predict overall survival (n=6) and recurrence (n=9). The C-index, sensitivity (SEN), and specificity (SPC) of the ML model developed using clinical features (CFs) for ICC detection were 0.762 (95% CI 0.728-0.796), 0.72 (95% CI 0.66-0.77), and 0.72 (95% CI 0.66-0.78), respectively, in the validation dataset. In contrast, the C-index, SEN, and SPC of the radiomics-based ML model for detecting ICC were 0.853 (95% CI 0.824-0.882), 0.80 (95% CI 0.73-0.85), and 0.88 (95% CI 0.83-0.92), respectively. The C-index, SEN, and SPC of ML constructed using both radiomics and CFs for diagnosing ICC were 0.912 (95% CI 0.889-0.935), 0.77 (95% CI 0.72-0.81), and 0.90 (95% CI 0.86-0.92). The deep learning-based model that integrated both radiomics and CFs yielded a notably higher C-index of 0.924 (0.863-0.984) in the task of detecting ICC. Additional analyses showed that radiomics demonstrated promising accuracy in predicting overall survival and recurrence, as well as in diagnosing microvascular invasion, gene mutations, PNI, LN positivity, and TLSs.

CONCLUSIONS: Radiomics-based ML demonstrates excellent accuracy in the clinical diagnosis of ICC. However, studies involving specific tasks, such as diagnosing PNI and TLSs, are still scarce. The limited research on deep learning has hindered both further analysis and the development of subgroup analyses across various models. Furthermore, challenges such as data heterogeneity and interpretability caused by segmentation and imaging parameter variations require further optimization and refinement. Future research should delve into the application of radiomics to enhance its clinical use. Its integration into clinical practice holds great promise for improving decision-making, boosting diagnostic and treatment accuracy, minimizing unnecessary tests, and optimizing health care resource usage.

PMID:40323647 | DOI:10.2196/69906

J Craniofac Surg. 2025 May 5. doi: 10.1097/SCS.0000000000011466. Online ahead of print.

ABSTRACT

OBJECTIVE: The aim of this study is to develop a multimodal machine learning model that integrates magnetic resonance imaging (MRI) radiomics, deep learning features, and clinical indexes to predict the 3-year postoperative disease-free survival (DFS) in pediatric patients with malignant tumors.

METHODS: A cohort of 260 pediatric patients with brain tumors who underwent R0 resection (aged ≤ 14 y) was retrospectively included in the study. Preoperative T1-enhanced MRI images and clinical data were collected. Image preprocessing involved N4 bias field correction and Z-score standardization, with tumor areas manually delineated using 3D Slicer. A total of 1130 radiomics features (Pyradiomics) and 511 deep learning features (3D ResNet-18) were extracted. Six machine learning models (eg, SVM, RF, LightGBM) were developed after dimensionality reduction through Lasso regression analysis, based on selected clinical indexes such as tumor diameter, GCS score, and nutritional status. Bayesian optimization was applied to adjust model parameters. The evaluation metrics included AUC, sensitivity, and specificity.

RESULTS: The fusion model (LightGBM) achieved an AUC of 0.859 and an accuracy of 85.2% in the validation set. When combined with clinical indexes, the final model's AUC improved to 0.909. Radiomics features, such as texture heterogeneity, and clinical indexes, including tumor diameter ≥ 5 cm and preoperative low albumin, significantly contributed to prognosis prediction.

CONCLUSION: The multimodal model demonstrated effective prediction of the 3-year postoperative DFS in pediatric brain tumors, offering a scientific foundation for personalized treatment.

PMID:40323639 | DOI:10.1097/SCS.0000000000011466

Jpn J Radiol. 2025 May 5. doi: 10.1007/s11604-025-01772-y. Online ahead of print.

ABSTRACT

PURPOSE: This study aims to elucidate correlation between heart volume on computed tomography (CT) and various health checkup examination data in the general population. Furthermore, this study aims to examine the utility of a deep-learning segmentation tool in the data-driven analysis of CT big data.

MATERIALS AND METHODS: Health checkup examination data and CT images acquired in 2013 and 2018 were retrospectively analyzed. We first quantified heart volume using a public deep-learning model, TotalSegmentator. The accuracy of segmentation was evaluated using Dice score on 30 randomly chosen images and annotation by a radiologist. Then, Spearman's partial correlation was calculated for 58 numerical items, and the analysis of covariance was performed for 13 categorical items, adjusting for the effect of gender, medication, height, weight, abdominal circumference, and age. The variables found to be significant proceeded to longitudinal analysis.

RESULTS: In the dataset, 7993 records were eligible for cross-sectional analysis and 1306 individuals were eligible for longitudinal analysis. Pulse rate was most strongly inversely correlated with the heart volume (Spearman's correlation coefficients ranging from - 0.29 to - 0.33). A 10 bpm increase in pulse rate was correlated with roughly a 0.5 percentage point decrease in the cardiothoracic ratio. Hemoglobin, hematocrit, total protein, albumin, and cholinesterase also showed weak inverse correlation. Five-year longitudinal analysis corroborated these findings.

CONCLUSIONS: We found that pulse rate was the strongest covariate of the heart volume on CT, rather than other cardiovascular-related variables such as blood pressure. The study also demonstrated the feasibility and utility of the artificial intelligence-assisted data-driven research on CT big data.

PMID:40323526 | DOI:10.1007/s11604-025-01772-y

J Sci Food Agric. 2025 May 5. doi: 10.1002/jsfa.14331. Online ahead of print.

ABSTRACT

BACKGROUND: Automated fruit defect detection plays a critical role in improving postharvest quality assessment and supporting decision-making in agricultural supply chains. Guava defect detection presents specific challenges because of diverse disease types, varying maturity levels and inconsistent environmental conditions. Although existing you only look once (YOLO)-based models have shown promise in agricultural detection tasks, they often face limitations in balancing detection accuracy, inference speed and computational efficiency, particularly in resource-constrained settings. This study addresses this gap by evaluating four YOLO models (YOLOv8s, YOLOv5s, YOLOv9s and YOLOv11n) for detecting defective guava fruits across five diseases (scab, canker, chilling injury, mechanical damage and rot), three maturity levels (mature, half-mature and immature) and healthy fruits.

RESULTS: Diverse datasets facilitated robust training and evaluation. YOLOv11n achieved the highest mAP50-95 (98.0%) and exhibited bounding box loss (0.0565), classification loss (0.2787), inference time (3.9 milliseconds) and detection speed (255 FPS). YOLOv5s had the highest precision (94.9%), while YOLOv9s excelled in recall (96.2%). YOLOv8s offered a balanced performance across metrics. YOLOv11n outperformed all models with a lightweight architecture (2.6 million parameters) and low computational cost (6.3 giga floating-point operations per second), making it suitable for resource-constrained applications.

CONCLUSION: These results highlight YOLOv11n's potential for agricultural applications, such as automated defect detection and quality control, which require high accuracy and real-time performance across diverse conditions. This analysis provides insights into deploying YOLO models for agricultural quality assessment to enhance the efficiency and reliability of postharvest management. © 2025 Society of Chemical Industry.

PMID:40322977 | DOI:10.1002/jsfa.14331

Pulm Ther. 2025 May 5. doi: 10.1007/s41030-025-00296-0. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) represents a clinical and therapeutic challenge characterized by progressive fibrosis and destruction of the lung architecture. The pathogenesis of IPF has been long debated; while it is generally believed that repeated lung injury and abnormal wound repair are the main pathogenetic mechanisms, clear understanding of disease development and efficacious treatment remain important unmet needs. Indeed, current standard of care (i.e., the antifibrotic drugs pirfenidone and nintedanib) can slow down lung function decline and disease progression without halting the disease. In the last 2 decades, several clinical trials in IPF have been completed mostly with negative results. Yet, unprecedented numbers of clinical trials of pharmacological interventions are currently being conducted. In this review, we summarize and critically discuss the current and future treatment landscape of IPF, with emphasis on the most promising developmental molecules.

PMID:40323570 | DOI:10.1007/s41030-025-00296-0

Aging (Albany NY). 2025 May 3;17. doi: 10.18632/aging.206243. Online ahead of print.

ABSTRACT

Apolipoprotein E (APOE) modifies human aging; specifically, the ε2 and ε4 alleles are among the strongest genetic predictors of longevity and Alzheimer's disease (AD) risk, respectively. However, detailed mechanisms for their influence on aging remain unclear. In the present study, we analyzed multi-omic association patterns across APOE genotypes, sex, and biological age (BA) axes in 2,229 community dwelling individuals. Our analysis, supported by validation in an independent cohort, identified diacylglycerols as the top APOE-associated plasma metabolites. However, despite the known opposing aging effects of the allele variants, both ε2- and ε4-carriers showed higher diacylglycerols compared to ε3-homozygotes. 'Omics association patterns of ε2-carriers and increased biological age were also counter-intuitively similar, displaying significantly increased associations between insulin resistance markers and energy-generating pathway metabolites. These results demonstrate the context-dependence of the influence of APOE, with ε2 potentially strengthening insulin resistance-like pathways in the decades prior to imparting its longevity benefits. Additionally, they provide an atlas of APOE-related 'omic associations and support the involvement of bioenergetic pathways in mediating the impact of APOE on aging.

PMID:40323280 | DOI:10.18632/aging.206243

J Biosci. 2025;50:31.

ABSTRACT

The switch in the predominantly expressed transcript isoform of the same gene has been identified as a significant factor in the progression of various types of cancer. These switches can impact the gain or loss of different 3'UTRs, which are hotspots for the binding of microRNAs (miRNAs) and RNA-binding proteins (RBPs). In this study, we found that in cancer-specific dominant expressing transcripts, the binding of miRNA and RBP is disrupted, suggesting that transcript switching could play a part in modulating post-transcriptional gene expression during the progression and development of cancer. Our spatial correlation analysis demonstrated that changes in miRNA and RBP binding, triggered by transcript switching, could interrupt their interplay. Additionally, statistical analysis revealed that local folding energy (LFE) is a key factor in changing miRNA and RBP interactions due to isoform switching. Overall, this study revealed that changes in cancerspecific transcripts could influence miRNA-RBP interactions due to alternations in the local RNA structure of the transcript caused by isoform switching, thereby leading to the dysregulation of crucial genes involved in the evolution and progression of cancer.

PMID:40323003

Nucleic Acids Res. 2025 May 5:gkaf387. doi: 10.1093/nar/gkaf387. Online ahead of print.

ABSTRACT

The WashU Epigenome Browser (https://epigenomegateway.wustl.edu/) is a web-based tool for exploring genomic data and providing visualization, investigation, and analysis of epigenomic datasets. Since its 2018 update, the redesigned user interface and newly developed features have enhanced how investigators interact with both the Browser and the extensive genomic data it hosts. The rapid evolution of the JavaScript ecosystem has presented new challenges and opportunities in maintaining and developing the WashU Epigenome Browser. In this update, we present a completely rewritten codebase. This new codebase minimizes the use of external libraries whenever possible, resulting in a significantly smaller code bundle size after production compilation. The reduced code size improves loading efficiency and boosts the Browser's performance, with improved scripting, graphics rendering, and painting performance. Lowering external dependencies also allows for faster and more straightforward installation. Additionally, the update includes a redesign of the user interface to further enhance user experience and features a new modular design in the codebase that enables the Browser to be exported as stand-alone modules for use in other web applications. Several novel track types for long-read methylation data and single-cell methylation data visualization have been added, and we continue to update and expand the data hubs we host for major consortia. We constructed the first data hub to systematically compare genomic data mapped to different genome assemblies, focusing on comparisons between hg38 and the first human T2T genome, chm13, using our new comparative genomics track function. The WashU Epigenome Browser also serves as a foundation for other genomics platforms, such as the WashU Virus Genome Browser, developed for SARS-COV-2 research, the WashU Comparative Epigenome Browser, and the WashU Repeat Browser.

PMID:40322916 | DOI:10.1093/nar/gkaf387

J Ocul Pharmacol Ther. 2025 May 5. doi: 10.1089/jop.2024.0178. Online ahead of print.

ABSTRACT

Purpose: The aim of this study was to identify and quantify the occurrence of corneal deposits caused by medications, utilizing data from the Food and Drug Administration Adverse Event Reporting System (FAERS). Methods: We conducted a retrospective analysis of the national FAERS database, focusing on instances of drug-induced corneal deposits reported between 2004 and the third quarter of 2023. Our methodology included applying the proportional reporting ratio, reporting odds ratio, empirical Bayes geometric mean, and information component in our disproportionality analysis. A signal was considered present if all four of these disproportionality metrics showed positive results. Results: Over the span of 20 years, our research identified 383 adverse event reports linked to corneal deposits associated with 349 different medications. The most common age-group of these reports involved patients over 65 years of age (32.4%), with equal distribution between male (40.0%) and female (42.8%) patients. Thirty-one medications showed a positive signal. Notably, drugs such as amiodarone (68 reports), prednisolone (60 reports), and timolol (54 reports) were most frequently mentioned. Cyclopentolate and chloramphenicol demonstrated robust statistical relevance in association with corneal deposits. Conclusions: Positive signals for drug-induced corneal deposits included both well-known medications such as amiodarone and lesser-studied medications such as prednisolone and timolol. Clinician awareness of these findings alongside further investigation is needed.

PMID:40322910 | DOI:10.1089/jop.2024.0178

Perspect Clin Res. 2025 Apr-Jun;16(2):61-68. doi: 10.4103/picr.picr_70_24. Epub 2024 Sep 10.

ABSTRACT

Drug repurposing, also known as drug repositioning or reprofiling, involves identifying new therapeutic uses for existing drugs beyond their original indications. Historical examples include sildenafil citrate transitioning to an erectile dysfunction treatment and thalidomide shifting from a sedative to an immunomodulatory agent. Advocates tout its potential to address unmet medical needs by expediting development, reducing costs, and using drugs with established safety profiles. However, concerns exist regarding specificity for new indications, safety, and regulatory exploitation. Ethical considerations include equitable access, informed consent when using drugs off-label, and transparency. Recent advancements include artificial intelligence (AI) applications, network pharmacology, and omics technologies. Clinical trials explore repurposed drugs' efficacy, with regulatory agencies facilitating approval. Challenges include intellectual property protection, drug target specificity, trial design complexities, and funding limitations. Ethical challenges encompass patient autonomy, potential conflicts of interest due to financial incentives for industries, and resource allocation. Future directions involve precision medicine, AI, and global collaboration. In conclusion, drug repurposing offers a promising pathway for therapeutic innovation but requires careful consideration of its complexities and ethical implications to maximize benefits and minimize risks.

PMID:40322475 | PMC:PMC12048090 | DOI:10.4103/picr.picr_70_24

Expert Opin Drug Deliv. 2025 May 5. doi: 10.1080/17425247.2025.2502022. Online ahead of print.

ABSTRACT

INTRODUCTION: Artificial intelligence (AI) has emerged as a transformative force in nanomedicine. revolutionizing drug delivery, diagnostics, and personalized treatment. While nanomedicine offers precise targeted drug delivery and reduced toxic effects, its clinical translation is hindered by biological complexity, unpredictable in vivo behavior, and inefficient trial-and-error approaches.

AREAS COVERED: This review covers the application of AI and Machine Learning (ML) across the nanomedicine development pipeline, starting from drug and target identification to nanoparticle design, toxicity prediction, and personalized dosing. Different AI/ML models like QSAR, MTK-QSBER, and Alchemite, along with data sources and high-throughput screening methods, have been explored. Real-world applications are critically discussed, including AI-assisted drug repurposing, controlled-release formulations, and cancer-specific delivery systems.

EXPERT OPINION: AI has emerged as an essential component in designing next-generation nanomedicine. Efficiently handling multidimensional datasets, optimizing formulations, and personalizing treatment regimens, it has sped up the innovation process. However, challenges like data heterogeneity, model transparency, and regulatory gaps remain. Addressing these hurdles through interdisciplinary efforts and emerging innovations like explainable AI and federated learning will pave the way for the clinical translation of AI-driven nanomedicine.

PMID:40321117 | DOI:10.1080/17425247.2025.2502022

J Biomol Struct Dyn. 2025 May 4:1-12. doi: 10.1080/07391102.2025.2497448. Online ahead of print.

ABSTRACT

Parkinson's disease (PD) is a prevalent neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra that leads to bradykinesia and rest tremors. While the molecular mechanisms underlying PD are not fully understood, rising evidence shows neuroinflammation as a key factor in dopaminergic neuron damage. The soluble epoxide hydrolase (sEH) has appeared as a key player in neuroinflammation associated with PD which represents itself as a promising drug target. Here, we employed a structure-based virtual screening methodology using repurposed drugs from the DrugBank database to identify high-affinity potential inhibitors of sEH. Results showed that two hit molecules, Fluspirilene and Penfluridol, demonstrated appreciable docking potential and specificity toward the sEH active site. These molecules exhibited favorable pharmacological properties and formed critical interactions with residues essential for sEH activity. Further, all-atom molecular dynamics (MD) simulations followed by principal component analysis and free energy landscape were carried out which provide deeper insights into the conformational stability and interaction mechanisms of sEH in complex with Fluspirilene and Penfluridol. The simulation results indicated that the interaction of sEH with Fluspirilene and Penfluridol contributed to the stabilization of its structure throughout the MD trajectories of 500 ns. These findings collectively suggest that Fluspirilene and Penfluridol hold potential as repurposed leads for the development of sEH inhibitors, which offer therapeutic implications for combating PD and other associated conditions.

PMID:40320778 | DOI:10.1080/07391102.2025.2497448

Data Brief. 2025 Apr 2;60:111535. doi: 10.1016/j.dib.2025.111535. eCollection 2025 Jun.

ABSTRACT

There has been a recent push to make public, aggregate, and increase coverage of bibliographic citation data. Here we describe uCite, a citation dataset containing 564 million PubMed citation pairs aggregated from the following nine sources: PubMed Central, iCite, OpenCitations, Dimensions, Microsoft Academic Graph, Aminer, Semantic Scholar, Lens, and OpCitance. Of these, 51 million (9%) were labeled unreliable, as determined by patterns of source discrepancies explained by ambiguous metadata, crosswalk, and typographical errors, citing future publications, and multi-paper documents. Each source contributes to improved coverage and reliability, but varies dramatically in precision and recall, estimates of which are contrasted with the Web of Science and Scopus herein.

PMID:40322502 | PMC:PMC12049819 | DOI:10.1016/j.dib.2025.111535

Drug Des Devel Ther. 2025 Apr 30;19:3589-3610. doi: 10.2147/DDDT.S505751. eCollection 2025.

ABSTRACT

INTRODUCTION: Venous thromboembolism (VTE) is a leading cause of cardiovascular-related deaths. Non-vitamin K antagonist oral anticoagulants (NOACs) offer effective therapy without injections or blood monitoring. This bibliometric analysis explores the research on NOACs for preventing VTE and pulmonary embolism.

METHODS: Literature up to July 20, 2024, was searched in Web of Science Core Collection. Citespace software was used for screening and analysis.

RESULTS: In this study, we analyzed 2124 articles and 767 reviews from 11,282 institutions across 528 countries and regions, encompassing 830 publications and 60 research directions. The USA led in publication count, followed by Germany and Canada. Cardiovascular System Cardiology, Hematology, and General Internal Medicine were the top research areas, while THROMBOSIS AND HAEMOSTASIS was the leading journal. From 2004 to 2024, we observed accelerated publication growth, particularly from 2008, highlighting the emergence of NOACs as a major research focus. Key contributors, including Bengt I. Eriksson, and major institutions like Harvard Medical School and University of Amsterdam, played pivotal roles in advancing anticoagulant research. Co-citation and keyword clustering analyses revealed research hotspots in NOACs, cancer-associated venous thromboembolism, stroke prevention, and COVID-19-related thrombotic events, reflecting a shift towards individualized anticoagulation therapy and the growing importance of NOACs in various clinical contexts.

CONCLUSION: The development of NOACs has progressed rapidly, with an increasing number of publications, indicating the lead research in the United States and other Western nations. Comparative studies on the safety and efficacy of NOACs have become a significant focus, shifting from traditional anticoagulants. Pharmacogenetics-guided use of NOACS shows new hope of precision medicine.

PMID:40322032 | PMC:PMC12050026 | DOI:10.2147/DDDT.S505751

Biomicrofluidics. 2025 May 1;19(3):034101. doi: 10.1063/5.0255847. eCollection 2025 May.

ABSTRACT

Remote health monitoring has the potential to enable individuals to take control of their own health and well-being and to facilitate a transition toward preventative and personalized healthcare. Sweat can be sampled non-invasively and contains a wealth of information about the metabolic state of an individual, making it an excellent candidate for remote health monitoring. An accurate, rapid, and low-cost biofluid characterization technique is required to enable the widespread use of remote health monitoring. We previously introduced microfluidic impedance spectroscopy for the detection of electrolyte concentration in fluids, whereby a novel device architecture, measurement method, and analysis technique were presented for the characterization of cationic species. The purely electrical nature of this measurement technique removes the intermediate steps inherent in common rival technologies such as optical and electrochemical sensing, offering a range of advantages. In this work, we investigate the effect of temperature on microfluidic impedance spectroscopy of ionic species commonly present in biofluids. We find that the impedance spectra and concentration determination are temperature-dependent; remote health monitoring devices must be calibrated appropriately as they are likely to experience temperature fluctuations. Importantly, we demonstrate the ability of the method to measure the concentration of anionic species alongside that of cationic species, enabling the detection of chloride and lactate, which are useful biomarkers for hydration, cystic fibrosis, fatigue, sepsis, and hypoperfusion. We show that the presence of neutral species does not impair accurate determination of ionic concentration, thus, demonstrating the suitability of microfluidic impedance spectroscopy for non-invasive biofluid characterization.

PMID:40322639 | PMC:PMC12048175 | DOI:10.1063/5.0255847

Open Respir Med J. 2025 Feb 12;19:e18743064341009. doi: 10.2174/0118743064341009241210045737. eCollection 2025.

ABSTRACT

Despite medical science advancements in recent years, pulmonary diseases are still hard to control and can be potentially life-threatening. These include asthma, COPD, lung cancer, cystic fibrosis, pneumonia, pleurisy, and sarcoidosis. These illnesses often cause severe breathing problems, which can be fatal if not treated properly. While some chemical drugs are used to treat these conditions, they can cause side effects and are not always effective. Herbal medicine offers an alternative treatment option with fewer side effects and has shown promise in treating respiratory issues. Certain medicinal plants, such as garlic (Allium sativum), hawthorn (Crataegus rhipidophylla), moringa (Moringa oleifera), and ashwagandha (Withania somnifera), may help manage lung diseases. Natural compounds found in plants, like apple polyphenol, ligustrazine, salidroside, resveratrol, and quercetin, can also help reduce symptoms. These plants and compounds work by reducing cell overgrowth, fighting oxidative stress, lowering inflammation, stopping tumor growth, improving blood flow, and relaxing the airways. This review outlines the types of plants and compounds that can be utilized in treating pulmonary conditions, along with their respective mechanisms of action.

PMID:40322495 | PMC:PMC12046236 | DOI:10.2174/0118743064341009241210045737

Bioinformation. 2025 Feb 28;21(2):173-176. doi: 10.6026/973206300210173. eCollection 2025.

ABSTRACT

Artificial intelligence (AI) driven automated image analysis accurately tracks orthodontic tooth movement by reducing reliance on time-consuming manual assessments. AI achieved 92% precision with a 0.25 mm error margin and a strong correlation (r = 0.94, p < 0.001) to manual measurements in a study of 100 patients. AI analysis took 3 seconds per image set, significantly faster than the 7-minute manual process (p < 0.001). Orthodontists rated AI reliability at 4.7/5, with 86% preferring AI-assisted monitoring. Thus, AI enhances treatment efficiency, standardization, and clinical decision-making.

PMID:40322709 | PMC:PMC12044183 | DOI:10.6026/973206300210173

Bioinformation. 2025 Feb 28;21(2):105-109. doi: 10.6026/973206300210105. eCollection 2025.

ABSTRACT

The integration of Artificial Intelligence (AI) in to the field of medicine is offering a new-age of updated diagnostics, prediction and treatment across multiple fields, addressing systemic disease including viral infections and cancer. The fields of Oral Pathology, Dermatology, Psychiatry and Cardiology are shifting towards integrating these algorithms to improve health outcomes. AI trained on biomarkers (e.g. salivary cf DNA) has shown to uncover the genetic linkage to disease and symptom susceptibility. AI-enhanced imaging has increased sensitivity in cancer and lesion detection, as well as detecting functional abnormalities not clinically identified. The integration of AI across fields enables a systemic approach to understanding chronic inflammation, a central driver in conditions like cardiovascular disease, diabetes and neuropsychiatric disorders. We propose that through the use of imaging data with biomarkers like cytokines and genetic variants, AI models can better trace the effects of inflammation on immune and metabolic disruptions. This can be applied to the pandemic response, where AI can model the cascading effects of systemic dysfunctions, refine predictions of severe outcomes and guide targeted interventions to mitigate the multi-systemic impacts of pathogenic diseases.

PMID:40322698 | PMC:PMC12044186 | DOI:10.6026/973206300210105

Cureus. 2025 May 3;17(5):e83421. doi: 10.7759/cureus.83421. eCollection 2025 May.

ABSTRACT

Breast cancer remains one of the leading causes of mortality among women, particularly in low- and middle-income countries, where limited healthcare access and delayed diagnosis contribute to poor outcomes. Deep learning, especially convolutional neural networks (CNNs), has shown remarkable efficacy in breast cancer detection through automated image analysis, reducing reliance on manual interpretation. This study provides a comprehensive review of recent advancements in CNN-based breast cancer detection, evaluating deep learning architectures, feature extraction techniques, and optimization strategies. A comparative analysis of CNNs, recurrent neural networks (RNNs), and hybrid models highlights their strengths, limitations, and applicability in medical image classification. Using a dataset of 569 instances with 33 tumor morphology features, various deep learning architectures - including CNNs, long short-term memory networks (LSTMs), and multilayer perceptrons (MLPs) - were implemented, achieving classification accuracies between 89% and 98%. The study underscores the significance of data augmentation, transfer learning, and feature selection in improving model performance. Hybrid CNN-based models demonstrated superior predictive accuracy by capturing spatial and sequential dependencies within tumor feature sets. The findings support the potential of AI-driven breast cancer detection in clinical applications, reducing diagnostic errors and improving early detection rates. Future research should explore transformer-based models, federated learning, and explainable AI techniques to enhance interpretability, robustness, and generalization across diverse datasets.

PMID:40322605 | PMC:PMC12049196 | DOI:10.7759/cureus.83421