Plant Cell Environ. 2025 Feb 17. doi: 10.1111/pce.15430. Online ahead of print.

ABSTRACT

Legumes have the beneficial capacity to establish symbiotic interactions with rhizobia, which provide their host plants with fixed nitrogen. However, in the presence of nitrogen, this process is rapidly repressed to avoid unnecessary investments of carbon in the symbiosis. Several players involved in regulating nodulation in response to nitrate availability have been identified, including peptide hormones, microRNAs and transcription factors. Nevertheless, how these molecular players are linked to each other and what underlying molecular mechanisms are at play to inhibit nodulation remain unresolved. Nitrate-mediated control of nodulation seems to differ between model legumes, such as Medicago and Lotus, compared to legume crops such as soybean. A deeper understanding of these regulatory processes, particularly in soybean, is expected to contribute to establishing increased nodulation efficiency in modern agricultural systems, hence improving sustainability by reducing the need for environmentally hazardous nitrogen fertilizers. This review describes the state of the art of nitrate-regulated nodulation in soybean, while drawing parallels with molecular mechanisms described in other legumes and addressing knowledge gaps that require future study.

PMID:39960031 | DOI:10.1111/pce.15430

J Biochem Mol Toxicol. 2025 Feb;39(2):e70172. doi: 10.1002/jbt.70172.

ABSTRACT

Cyclophosphamide (CYC) is one of the most potent antineoplastic drugs; however, hepatonephrotoxicity, observed following its use, remains one of its most severe side effects. Previous studies have reported that syringaldehyde (SYA), a flavonoid compound, exhibits anti-inflammatory and antioxidant properties. However, it is unclear whether SYA has any effects on hepatonephrotoxicity caused by the side effects of antineoplastic drugs. In the present research, we thoroughly evaluated the effects of SYA on cyclophosphamide-induced hepatonephrotoxicity in a mouse model, focusing on Nrf2/HO-1 pathway activation. In the present study, SYA (25 and 50 mg/kg, p.o.) and CYC (30 mg/kg, i.p.) were delivered to male mice for 10 days to induce hepatonephrotoxicity. SYA treatment alleviated the elevated levels of AST, ALT, BUN, and creatinine caused by CYC. It further suppressed lipid peroxidation by lowering MDA levels and enhanced antioxidant defense by elevating GSH, SOD, and CAT levels. Additionally, SYA increased the mRNA expression levels of HO-1, Nrf2, and Bcl-2, which had been reduced due to oxidative stress, inflammatory, and apoptotic pathways, while suppressing the elevated gene expression levels of NFκB, TNF-α, Bax, and Cas-3. Furthermore, SYA regulated the altered protein expression levels of Nrf2, Cas-3, Bax, and Bcl-2 induced by CYC. Microscopically, SYA also mitigated liver and kidney tissue damage caused by CYC. In conclusion, SYA significantly reduced CYC-induced hepatonephrotoxicity by inhibiting inflammation, oxidative stress, and apoptosis by employing the Nrf2/NFκB/HO-1 pathway. These findings indicate that SYA has the possibility as a treatment option agent in the case of prevention of liver and kidney damage.

PMID:39959927 | DOI:10.1002/jbt.70172

In Silico Pharmacol. 2025 Feb 13;13(1):27. doi: 10.1007/s40203-025-00307-7. eCollection 2025.

ABSTRACT

The global spread of monkeypox, caused by the double-stranded DNA monkeypox virus (MPXV), has underscored the urgent need for effective antiviral treatments. In this study, we aim to identify a potent inhibitor for MPXV DNA polymerase (DNAP), a critical enzyme in the virus replication process. Using a computational drug repurposing approach, we performed a virtual screening of 1615 FDA-approved drugs based on drug-likeness and molecular docking against DNAP. Among these, 1430 compounds met Lipinski's rule of five for drug-likeness, with Doxycycline emerging as the most promising competitive inhibitor, binding strongly to the DNAP active site with a binding affinity of - 9.3 kcal/mol. This interaction involved significant hydrogen bonds, electrostatic interactions, and hydrophobic contacts, with Doxycycline demonstrating a stronger affinity than established antivirals for smallpox, including Cidofovir, Brincidofovir, and Tecovirimat. Stability and flexibility analyses through a 200 ns molecular dynamics simulation and normal mode analysis confirmed the robustness of Doxycycline binding to DNAP. Overall, our results suggest Doxycycline as a promising candidate for monkeypox treatment, though additional experimental and clinical studies are needed to confirm its therapeutic potential and clinical utility.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40203-025-00307-7.

PMID:39958784 | PMC:PMC11825436 | DOI:10.1007/s40203-025-00307-7

Mini Rev Med Chem. 2025 Feb 14. doi: 10.2174/0113895575360959250117073046. Online ahead of print.

ABSTRACT

Sleep disorders and the resultant sleep deprivation (SD) are very common nowadays, resulting in depressed mood, poor memory and concentration, and various important changes in health, performance and safety. They may provoke further impairment of the cell lining of the blood vessels, as acting as a risk factor for cardiovascular disease (CVD) onset and progression. SD may lead to low neuronal regaining and plasticity, drastically affecting brain function. Thus, SD is a known risk factor for mental, behavioral and developmental disorders. Due to the inflammatory and oxidative stressful nature of SD, immune response modulation and antioxidants could be another therapeutic approach, apart from the already known symptomatic treatment with sedatives. Additionally, many drugs approved for other indications and under investigation, have been revisited due to their wide array of pharmacological activities. This review summarizes the main aspects of SD pathology and SD interrelated comorbidities and presents direct and indirect antioxidant molecules and drugs with multi-targeting potential that could assist in the prevention or management of these factors. A number of research groups have investigated well-known antioxidant compounds with multi-targeting cores, combining structural characteristics with properties including antiinflammatory, metal chelatory, gene transcription and immune modulatory that may add towards the effective SD and its associated comorbidities treatment.

PMID:39957704 | DOI:10.2174/0113895575360959250117073046

Pharmacogenomics. 2025 Feb 16:1-10. doi: 10.1080/14622416.2025.2463866. Online ahead of print.

ABSTRACT

AIM: To share the experience of a US national reference laboratory, offering genotyping for TPMT and NUDT15, TPMT enzyme phenotyping and detection of thiopurine metabolites.

METHODS: Retrospective review of archived datasets related to thiopurines drug therapy including patients' data that underwent TPMT and NUDT15 genotyping, and smaller data sets where genotyping was performed with TPMT enzyme levels (phenotyping) +/- therapeutic drug monitoring (TDM).

RESULTS: Thirteen percent of patients had variants in one or both genes tested. Testing for NUDT15 revealed 3.9% additional patients requiring thiopurines dosing recommendations. A correlation between TPMT enzyme activity and TPMT polymorphisms (odds ratio OD = 71.41, p-value <0.001) and between older age and higher enzyme levels (OD = 0.98, p-value = 0.002) was identified. No correlation between sex and TPMT enzyme levels, nor between TPMT genotyping and the level of thiopurine metabolites was found.

CONCLUSION: Adding NUDT15 to TPMT genotyping, identified additional 3.9% patients to benefit from thiopurine dose modifications. A significant correlation between genetic variants in TPMT and TPMT enzyme levels and between age and enzyme levels was established, while no correlation was identified between sex and enzyme levels nor between TPMT variation and thiopurine metabolites. Providers rely more significantly on genotyping only approach, rather than genotyping and phenotyping.

PMID:39957149 | DOI:10.1080/14622416.2025.2463866

Br J Clin Pharmacol. 2025 Feb 16. doi: 10.1002/bcp.70004. Online ahead of print.

ABSTRACT

AIMS: Phenoconversion, a genotype-phenotype mismatch, challenges a successful implementation of personalized medicine. The aim of this study was to detect and determine phenoconversion using the solanidine metabolites 3,4-seco-solanidine-3,4-dioic acid (SSDA) and 4-OH-solanidine as diet-derived cytochrome P450 2D6 (CYP2D6) biomarkers in a geriatric, multimorbid cohort with high levels of polypharmacy.

METHODS: Blood samples and data of geriatric, multimedicated patients were collected during physician counsel (CT: NCT05247814). Solanidine and its metabolites were determined via liquid chromatography/tandem mass spectrometry and used for CYP2D6 phenotyping. CYP2D6 genotyping was performed and activity scores (AS) were assigned. Complete medication intake was assessed. A shift of the AS predicted via genotyping as measured by phenotyping was calculated.

RESULTS: Solanidine and its metabolites were measured in 88 patients with complete documentation of drug use. Patients had a median age of 83 years (interquartile range [IQR] 77-87) and the majority (70.5%, n = 62) were female. Patients took a median of 15 (IQR 12-17) medications. The SSDA/solanidine metabolic ratio correlated significantly with the genotyping-derived AS (P < .001) and clearly detected poor metabolizers. In the model adjusted for age, sex, Charlson Comorbidity Index and estimated glomerular filtration rate each additional CYP2D6 substrate/inhibitor significantly lowered the expected AS by 0.53 (95% confidence interval 0.85-0.21) points in patients encoding functional CYP2D6 variants (R2 = 0.242).

CONCLUSIONS: Phenotyping of CYP2D6 activity by measurement of diet-derived biomarkers elucidates phenoconversion in geriatric patients. These results might serve as a prerequisite for the validation and establishment of a bedside method to measure CYP2D6 activity in multimorbid patients for successful application of personalized drug prescribing.

PMID:39957076 | DOI:10.1002/bcp.70004

ACS Omega. 2025 Jan 28;10(5):4336-4352. doi: 10.1021/acsomega.4c06520. eCollection 2025 Feb 11.

ABSTRACT

1,2,4-Oxadiazoles are well recognized for their exceptional physical, chemical, and pharmacokinetic properties, making them promising candidates for various therapeutic applications. These include treatments for cystic fibrosis, Duchenne muscular dystrophy, Alzheimer's disease, and a broad spectrum of other therapeutic interventions such as antituberculosis, anticancer, antibiotic, anti-inflammatory, and anticonvulsant activities. In this study, single crystals of a novel 1,2,4-oxadiazole derivative, methyl-4-(5-(4-(octyloxy)phenyl)-1,2,4-oxadiazol-3-yl)benzoate, were grown by a slow evaporation technique. The structural elucidation was performed using X-ray diffraction analysis, confirming the compound's crystalline structure in the triclinic system. The analysis revealed a linear conformation with bond lengths closely aligned with Cambridge Structural Database (CSD) averages, signifying high precision in the molecular structure. A detailed CSD study identified nine principal configurations of the phenyl octyloxy moiety, underscoring the structural diversity of the compound. Hirshfeld surface analysis highlighted the predominance of C-H···O and C-H···π interactions, with dispersion energy playing a critical role in stabilizing the crystal lattice. Docking studies against key microbial targets, particularly E. coli FabH, demonstrated superior binding energies, suggesting significant antimicrobial potential. The comprehensive suite of structural and computational analyses underscores the potential of the synthesized 1,2,4-oxadiazole derivative, which may be one of the promising candidates for antimicrobial drug development. Future in vitro, in vivo studies will be supportive in optimizing the derivative for enhanced efficacy and further elucidating its pharmacological mechanisms, paving the way for potential clinical applications. This study not only provides insights into the structural and functional properties of a novel 1,2,4-oxadiazole derivative but also highlights its promising role in antimicrobial drug discovery.

PMID:39959081 | PMC:PMC11822514 | DOI:10.1021/acsomega.4c06520

Liver Res. 2024 Sep 16;8(3):131-140. doi: 10.1016/j.livres.2024.09.003. eCollection 2024 Sep.

ABSTRACT

Compared with the widely used rodents, pigs are anatomically, physiologically, and genetically more similar to humans, making them high-quality models for the study of liver diseases. Here, we review the latest research progress on pigs as a model of human liver disease, including methods for establishing them and their advantages in studying cystic fibrosis liver disease, acute liver failure, liver regeneration, non-alcoholic fatty liver disease, liver tumors, and xenotransplantation. We also emphasize the importance of genetic engineering techniques, mainly the CRISPR/Cas9 system, which has greatly enhanced the utility of porcine models as a tool for substantially advancing liver disease research. Genetic engineering is expected to propel the pig as one of the irreplaceable animal models for future biomedical research.

PMID:39957748 | PMC:PMC11771255 | DOI:10.1016/j.livres.2024.09.003

Data Brief. 2025 Jan 27;59:111328. doi: 10.1016/j.dib.2025.111328. eCollection 2025 Apr.

ABSTRACT

Leaf dimensioning is relevant for analyzing plant responses to several conditions such as soil fertility, availability of light, agricultural pesticide effect, and access to water in the soil or periods of drought. In this paper, we present a dataset composed of 6981 images of 612 common bean leaves (Phaseolus vulgaris). We captured the images of each leaf accompanied by a fiducial marker and annotated the known leaf dimensions (area, perimeter, length, and width). We provide annotations concerning image segmentation, known area uniformly distributed over the leaf region, real area of the marker region, marker pose, capture conditions, and camera calibration. This dataset can be useful for developing deep learning algorithms for leaf dimensioning and related problems. Therefore, there is a potential to contribute to computer vision and plant physiology researchers and specialists.

PMID:39959655 | PMC:PMC11830349 | DOI:10.1016/j.dib.2025.111328

Middle East Afr J Ophthalmol. 2024 Dec 2;30(4):189-202. doi: 10.4103/meajo.meajo_154_24. eCollection 2023 Oct-Dec.

ABSTRACT

Numerous artificial intelligence (AI) models, including deep learning techniques, are being developed to segment choroids in optical coherence tomography (OCT) images. However, there is a need for consensus on which specific models to use, requiring further synthesis of their efficacy and role in choroid visualization in myopic patients. A systematic literature search was conducted on three main databases (PubMed, Web of Science, and Scopus) using the search terms: "Machine learning" OR "Artificial Intelligence" OR "Deep learning" AND "Myopia" AND "Choroid" OR "Choroidal" from inception to February 2024 removing duplicates. A total of 12 studies were included. The populations included myopic patients with varying degrees of myopia. The AI models applied were primarily deep learning models, including U-Net with a bidirectional Convolutional Long Short-Term Memory module, LASSO regression, Attention-based Dense U-Net network, ResNeSt101 architecture training five models, and Mask Region-Based Convolutional Neural Network. The reviewed AI models demonstrated high diagnostic accuracy, including sensitivity, specificity, and area under the curve values, in identifying and assessing myopia-related changes. Various biomarkers were assessed, such as choroidal thickness, choroidal vascularity index, choroidal vessel volume, luminal volume, and stromal volume, providing valuable insights into the structural and vascular changes associated with the condition. The integration of AI models in ophthalmological imaging represents a significant advancement in the diagnosis and management of myopia. The high diagnostic accuracy and efficiency of these models underscore their potential to revolutionize myopia care, improving patient outcomes through early detection and precise monitoring of disease progression. Future studies should focus on standardizing AI methodologies and expanding their application to broader clinical settings to fully realize their potential in ophthalmology.

PMID:39959595 | PMC:PMC11823532 | DOI:10.4103/meajo.meajo_154_24

IEEE Sens J. 2024 Jun 15;24(12):19590-19600. doi: 10.1109/jsen.2024.3396052. Epub 2024 May 7.

ABSTRACT

Nocturnal blood pressure (BP) monitoring offers valuable insights into various aspects of human wellbeing, particularly cardiovascular health. Despite recent advancements in medical technology, there remains a pressing need for a non-invasive, cuffless, and less burdensome method for overnight BP measurements. A range of machine learning models have been developed to estimate daytime BP using photoplethysmography (PPG), a readily available sensor embedded in modern wearable devices. However, investigations into nocturnal BP estimation, especially concerning long-term data patterns during sleep, are still lacking. This paper investigates the estimation of nocturnal BP from overnight PPG signals collected in a clinical-grade sleep laboratory setting. To address this, we propose SleepBP-Net, a lightweight time-distributed convolutional recurrent network. This novel model leverages long-term patterns within PPG waveforms to estimate systolic and diastolic BP (SBP and DBP), considering Portapres BP measurements as a reference. Our experiments, based on leave-one-subject-out validation on 1-minute sequences of PPG, resulted in a mean absolute error (MAE) of 15.7 mmHg (SBP) and 12.1 mmHg (DBP). Model personalization improved the results to 7.8 mmHg (SBP) and 5.9 mmHg (DBP). Further enhancements were observed when extending the sequence length to 30 minutes, resulting in MAE values of 7.2 mmHg (SBP) and 5.7 mmHg (DBP). These findings underscore the significance of learning long-term temporal patterns from sleep PPG data. Additionally, we demonstrate the superiority of hybrid convolutional recurrent networks over their convolutional network counterparts. Based on our results, SleepBP-Net holds promise for unobtrusive real-world nocturnal BP estimation, particularly in scenarios where computational efficiency is crucial.

PMID:39959563 | PMC:PMC11824277 | DOI:10.1109/jsen.2024.3396052

Transl Anim Sci. 2025 Feb 5;9:txae168. doi: 10.1093/tas/txae168. eCollection 2025.

ABSTRACT

The aim of the study was to deploy computer vision models for real-time detection of digital dermatitis (DD) lesions in cows using Android or iOS mobile applications. Early detection of DD lesions in dairy cows is crucial for prompt treatment and animal welfare. Android and iOS apps could facilitate routine and early DD detection in cows' feet on dairy and beef farms. Upon detecting signs of DD, dairy farmers could implement preventive and treatment methods, including foot baths, topical treatment, hoof trimming, or quarantining cows affected by DD to prevent its spread. We applied transfer-learning to DD image data for 5 lesion classes, M0, M4H, M2, M2P, and M4P, on pretrained YOLOv5 model architecture using COCO-128 pretrained weights. The combination of localization loss, classification loss, and objectness loss was used for the optimization of prediction performance. The custom DD detection model was trained on 363 images of size 416 × 416 pixels and tested on 46 images. During model training, data were augmented to increase model robustness in different environments. The model was converted into TFLite format for Android devices and CoreML format for iOS devices. Techniques such as quantization were implemented to improve inference speed in real-world settings. The DD models achieved a mean average precision (mAP) of 0.95 on the test dataset. When tested in real-time, iOS devices resulted in Cohen's kappa value of 0.57 (95% CI: 0.49 to 0.65) averaged across the 5 lesion classes denoting the moderate agreement of the model detection with human investigators. The Android device resulted in a Cohen's kappa value of 0.38 (95% CI: 0.29 to 0.47) denoting fair agreement between model and investigator. Combining M2 and M2P classes and M4H and M4P classes resulted in a Cohen's kappa value of 0.65 (95% CI: 0.54 to 0.76) and 0.46 (95% CI: 0.35 to 0.57), for Android and iOS devices, respectively. For the 2-class model (lesion vs. non-lesion), a Cohen's kappa value of 0.74 (95% CI: 0.63 to 0.85) and 0.65 (95% CI: 0.52 to 0.78) was achieved for iOS and Android devices, respectively. iOS achieved a good inference time of 20 ms, compared to 57 ms on Android. Additionally, we deployed models on Ultralytics iOS and Android apps giving kappa scores of 0.56 (95% CI: 0.48 to 0.64) and 0.46 (95% CI: 0.37 to 0.55), respectively. Our custom iOS app surpassed the Ultralytics apps in terms of kappa score and confidence score.

PMID:39959562 | PMC:PMC11829201 | DOI:10.1093/tas/txae168

Heliyon. 2025 Jan 25;11(3):e42257. doi: 10.1016/j.heliyon.2025.e42257. eCollection 2025 Feb 15.

ABSTRACT

Congenital heart disease (CHD), impacting around 1 % of infants worldwide, constitutes a significant healthcare challenge. Early detection is crucial, however constrained by the intricacies of conventional diagnostic techniques such as auscultation and echocardiography. This research presents a tailored one-dimensional convolutional neural network (1D-CNN) for the classification of phonocardiogram (PCG) signals into normal or abnormal categories, providing an automated and efficient solution for congenital heart disease (CHD) diagnosis. The model was trained on a composite dataset consisting of local pediatric PCG signals and publicly accessible dataset. Preprocessing methods, such as low- and high-pass filtering (60-650 Hz), resampling, and noise reduction, were utilized to enhance signal quality. Data augmentation techniques, including chunking, padding, and pitch-shifting, were employed to rectify dataset imbalances and improve model efficacy. Experimental results indicate substantial enhancements, attaining an accuracy of 98.56 %, precision of 98.56 %, F1 score of 98.55 %, sensitivity of 0.98, and specificity of 0.99. The comparative analysis demonstrates the proposed approach's superiority over current methods regarding accuracy and reliability. The research highlights the promise of combining modern signal processing with deep learning for efficient CHD screening. The suggested model exhibits outstanding performance yet, issues like dataset variability and noise persist. Future endeavors involve extending to multiclass categorization and assessing performance across a wider range of medical problems. This study represents a significant advancement in accessible, automated CHD diagnoses, enhancing clinical competence to elevate pediatric treatment.

PMID:39959496 | PMC:PMC11830292 | DOI:10.1016/j.heliyon.2025.e42257

Front Plant Sci. 2025 Jan 31;16:1533206. doi: 10.3389/fpls.2025.1533206. eCollection 2025.

ABSTRACT

INTRODUCTION: Ratoon rice, as a high-efficiency rice cultivation mode, is widely applied around the world. Mechanical righting of rolled rice stubble can significantly improve yield in regeneration season, but lack of automation has become an important factor restricting its further promotion.

METHODS: In order to realize automatic navigation of the righting machine, a method of fusing an instance segmentation model and a monocular depth prediction model was used to realize monocular localization of the rolled rice stubble rows in this study.

RESULTS: To achieve monocular depth prediction, a depth estimation model was trained on training set we made, and absolute relative error of trained model on validation set was only 7.2%. To address the problem of degradation of model's performance when migrated to other monocular cameras, based on the law of the input image's influence on model's output results, two optimization methods of adjusting inputs and outputs were used that decreased the absolute relative error from 91.9% to 8.8%. After that, we carried out model fusion experiments, which showed that CD (chamfer distance) between predicted 3D coordinates of navigation points obtained by fusing the results of the two models and labels was only 0.0990. The CD between predicted point cloud of rolled rice stubble rows and label was only 0.0174.

PMID:39959348 | PMC:PMC11825797 | DOI:10.3389/fpls.2025.1533206

ACS Omega. 2025 Jan 28;10(5):5105-5110. doi: 10.1021/acsomega.4c11266. eCollection 2025 Feb 11.

ABSTRACT

The integration of artificial intelligence (AI) into natural product science is an exciting and rapidly evolving area of research. By combining classical chemistry and biology with deep learning, these technologies have significantly improved research efficiency, particularly in overcoming laborious and time-consuming processes. Recently, there has been growing interest in leveraging multimodal algorithms to integrate biologically relevant yet mathematically disparate data sets in order to reorganize knowledge graphs. However, to the best of our knowledge, no studies have yet applied this approach specifically within the natural product field. This is largely because correlating multimodal natural product data is challenging due to their high degree of fragmentation. Here, we present MultiT2, an algorithm that connects these disparate data from bacterial aromatic polyketides, which form a medically important natural product family, as a showcase. Through a large-scale causal inference process, this approach aims to transcend mere prediction, unlocking new dimensions in the natural product discovery and research domains.

PMID:39959056 | PMC:PMC11822507 | DOI:10.1021/acsomega.4c11266

Arch Acad Emerg Med. 2024 Dec 26;13(1):e23. doi: 10.22037/aaemj.v13i1.2479. eCollection 2025.

ABSTRACT

INTRODUCTION: Identifying the people who try to hide illegal substances in the body for smuggling is of considerable importance in forensic medicine and poisoning. This study aimed to develop a new diagnostic method using artificial intelligence to detect body packs in real-time Abdominal computed tomography (CT) scans.

METHODS: In this cross-sectional study, abdominal CT scan images were employed to create a machine learning-based model for detecting body packs. A single-step object detection called RetinaNet using a modified neck (Proposed Model) was performed to achieve the best results. Also, an angled Bbox (oriented bounding box) in the training dataset played an important role in improving the results.

RESULTS: A total of 888 abdominal CT scan images were studied. Our proposed Body Packs Detection (BPD) model achieved a mean average precision (mAP) value of 86.6% when the intersection over union (IoU) was 0.5, and a mAP value of 45.6% at different IoU thresholds (from 0.5 to 0.95 in steps of 0.05). It also obtained a Recall value of 58.5%, which was the best result among the standard object detection methods such as the standard RetinaNet.

CONCLUSION: This study employed a deep learning network to identify body packs in abdominal CT scans, highlighting the importance of incorporating object shape and variability when leveraging artificial intelligence in healthcare to aid medical practitioners. Nonetheless, the development of a tailored dataset for object detection, like body packs, requires careful curation by subject matter specialists to ensure successful training.

PMID:39958959 | PMC:PMC11829241 | DOI:10.22037/aaemj.v13i1.2479

Phys Imaging Radiat Oncol. 2025 Jan 26;33:100708. doi: 10.1016/j.phro.2025.100708. eCollection 2025 Jan.

ABSTRACT

BACKGROUND AND PURPOSE: The development of Magnetic Resonance Imaging (MRI)-only Radiotherapy (RT) represents a significant advancement in the field. This study introduces a Deep Learning (DL) algorithm designed to quickly generate synthetic CT (sCT) images from low-field MR images in the brain, an area not yet explored.

METHODS: Fifty-six patients were divided into training (32), validation (8), and test (16) groups. A conditional Generative Adversarial Network (cGAN) was trained on pre-processed axial paired images. sCTs were validated using mean absolute error (MAE) and mean error (ME) calculated within the patient body. Intensity Modulated Radiation Therapy (IMRT) plans were optimised on simulation MRI and calculated considering sCT and original CT as electron density (ED) map. Dose distributions using sCT and CT were compared using global gamma analysis at different tolerance criteria (2 %/2mm and 3 %/3mm) and evaluating the difference in estimating different Dose Volume Histogram (DVH) parameters for target and organs at risk (OARs).

RESULTS: The network generated sCTs of each single patient in less than two minutes (mean time = 103 ± 41 s). For test patients, the MAE was 62.1 ± 17.7 HU, and the ME was -7.3 ± 13.4 HU. Dose parameters on sCTs were within 0.5 Gy of those on original CTs. Gamma passing rates 2 %/2mm, and 3 %/3mm criteria were 99.5 %±0.5 %, and 99.7 %±0.3 %, respectively.

CONCLUSION: The proposed DL algorithm generates in less than 2 min accurate sCT images in the brain for online adaptive radiotherapy, potentially eliminating the need for CT simulation in MR-only workflows for brain treatments.

PMID:39958708 | PMC:PMC11830347 | DOI:10.1016/j.phro.2025.100708

Front Immunol. 2025 Jan 27;16:1530279. doi: 10.3389/fimmu.2025.1530279. eCollection 2025.

ABSTRACT

BACKGROUND: This study aimed to develop a multi-modality model by incorporating pretreatment computed tomography (CT) radiomics and pathomics features along with clinical variables to predict pathologic complete response (pCR) to neoadjuvant chemoimmunotherapy in patients with locally advanced esophageal cancer (EC).

METHOD: A total of 223 EC patients who underwent neoadjuvant chemoimmunotherapy followed by surgical intervention between August 2021 and December 2023 were included in this study. Radiomics features were extracted from contrast-enhanced CT images using PyrRadiomics, while pathomics features were derived from whole-slide images (WSIs) of pathological specimens using a fine-tuned deep learning model (ResNet-50). After feature selection, three single-modality prediction models and a combined multi-modality model integrating two radiomics features, 11 pathomics features, and two clinicopathological features were constructed using the support vector machine (SVM) algorithm. The performance of the models were evaluated using receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analysis (DCA). Shapley values were also utilized to explain the prediction model.

RESULTS: The predictive capability of the multi-modality model in predicting pCR yielded an area under the curve (AUC) of 0.89 (95% confidence interval [CI], 0.75-1.00), outperforming the radiomics model (AUC 0.70 [95% CI 0.54-0.85]), pathomics model (AUC 0.77 [95% CI 0.53-1.00]), and clinical model (AUC 0.63 [95% CI 0.46-0.80]). Additionally, both the calibration plot and DCA curves support the clinical utility of the integrated multi-modality model.

CONCLUSIONS: The combined multi-modality model we propose can better predict the pCR status of esophageal cancer and help inform clinical treatment decisions.

PMID:39958355 | PMC:PMC11827421 | DOI:10.3389/fimmu.2025.1530279

J Inflamm Res. 2025 Feb 11;18:1993-2009. doi: 10.2147/JIR.S489210. eCollection 2025.

ABSTRACT

BACKGROUND: Studies suggest that immune and inflammation processes may be involved in the development of idiopathic pulmonary fibrosis (IPF); however, their roles remain unclear. This study aims to identify key genes associated with immune response and inflammation in IPF using bioinformatics.

METHODS: We identified differentially expressed genes (DEGs) in the GSE93606 dataset and GSE28042 dataset, then obtained differentially expressed immune- and inflammation-related genes (DE-IFRGs) by overlapping DEGs. Two machine learning algorithms were used to further screen key genes. Genes with an area under curve (AUC) of > 0.7 in receiver operating characteristic (ROC) curves, significant expression and consistent trends across datasets were considered key genes. Based on these key genes, we carried out nomogram construction, enrichment and immune analyses, regulatory network mapping, drug prediction, and expression verification.

RESULTS: 27 DE-IFRGs were identified by intersecting 256 DEGs, 1793 immune-related genes, and 1019 inflammation-related genes. Three genes (RNASE3, S100A12, S100A8) were obtained by crossing two machine algorithms (Boruta and LASSO),which had good diagnostic performance with AUC values. These key genes were all enriched in the same pathways, such as GOCC_azurophil_granule, IL-12 signalling and production in macrophages is the pathway with the strongest role for key genes. Six distinct immune cells, including naive CD4 T cells, T cells CD4 memory resting, T cells regulatory (Tregs), Monocytes, Macrophages M2, Neutrophils were identified. Real-time quantitative polymerase chain reaction (RT-qPCR) results were consistent with the training and validation sets, and the expression of these key genes was significantly upregulated in the IPF samples.

CONCLUSION: This study identified three key genes (RNASE3, S100A12 and S100A8) associated with immune response and inflammation in IPF, providing valuable insights into the diagnosis and treatment of IPF.

PMID:39959639 | PMC:PMC11829586 | DOI:10.2147/JIR.S489210

Cureus. 2025 Jan 16;17(1):e77522. doi: 10.7759/cureus.77522. eCollection 2025 Jan.

ABSTRACT

Bird fancier's lung (BFL) is a subtype of hypersensitivity pneumonitis (HP), an immune-mediated interstitial lung disease (ILD) resulting from the repeated inhalation of avian proteins found in bird droppings, feathers, and serum. Diagnosing BFL is challenging due to nonspecific symptoms that overlap with other ILDs like idiopathic pulmonary fibrosis and sarcoidosis. This complexity is heightened during pandemics such as coronavirus disease 2019 (COVID-19), where respiratory symptoms may be misattributed to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, leading to diagnostic anchoring and delays in appropriate management. High-resolution computed tomography (HRCT) is pivotal in detecting subtle pulmonary changes, characteristic of HP, surpassing standard chest radiographs. We present the case of a 43-year-old male pigeon keeper with an eight-week history of progressive dyspnea on exertion and intermittent chest pain. Despite unremarkable chest X-rays, HRCT revealed bilateral diffuse centrilobular nodules, patchy ground-glass opacities, and a mosaic attenuation pattern without fibrosis, consistent with acute HP. A thorough occupational history uncovered significant avian antigen exposure, and a family history suggested genetic susceptibility. The patient was diagnosed with BFL and treated with a tapering regimen of oral corticosteroids, starting at 40 mg/day. He was advised to cease pigeon keeping and avoid future avian exposure. Significant symptomatic improvement occurred within three months. Follow-up imaging over one year confirmed stable lung parenchyma with no disease progression or recurrence. This case underscores the importance of incorporating detailed occupational histories and utilizing advanced imaging modalities like HRCT when standard imaging is inconclusive. Early identification and intervention are crucial to prevent progression to chronic HP and irreversible fibrosis. Management should focus on reducing inflammation with corticosteroids and implementing strict environmental controls to prevent re-exposure. Long-term follow-up is essential to monitor for recurrence and maintain remission. Clinicians should remain vigilant for alternative diagnoses during pandemics to avoid diagnostic anchoring. This case contributes to the evidence supporting HRCT's critical role in early HP detection and emphasizes heightened clinical awareness of occupational lung diseases. A multidisciplinary approach involving pulmonologists, radiologists, and occupational medicine specialists is key to optimizing outcomes in HP and other ILDs.

PMID:39958101 | PMC:PMC11830419 | DOI:10.7759/cureus.77522