Invest Ophthalmol Vis Sci. 2025 May 1;66(5):17. doi: 10.1167/iovs.66.5.17.

ABSTRACT

PURPOSE: Amid efforts to understand spaceflight associated neuro-ocular syndrome (SANS), uncovering the role of the choroid in its etiology is challenged by the accuracy of image segmentation. The present study extended deep learning-based choroid quantification from optical coherence tomography (OCT) to the characterization of pulsatile and topological changes in the macular plane and investigated changes in response to prolonged microgravity exposure.

METHODS: We analyzed OCT macular videos and volumes acquired from astronauts before, during, and after long-duration spaceflight. Deep learning models were fine-tuned for choroid segmentation and combined with further image processing toward vascularity quantification. Statistical analysis was performed to determine changes in time-dependent and spatially averaged variables from preflight baseline.

RESULTS: For 12 astronauts with a mean age of 47 ± 9 years, there were significant increases in choroid thickness and luminal area (LA) averaged over OCT macular video segments. There was also a significant increase in pulsatile LA. For a subgroup of six astronauts for whom inflight imaging was available, choroid volume, luminal volume, and the choroidal vascularity index over the macular region all increased significantly during spaceflight.

CONCLUSIONS: The findings suggest that localized choroid pulsatile changes occur following prolonged microgravity exposure. They show that the choroid vessels expand in a manner similar to the choroid layer across the macular region during spaceflight, with a relative increase in the space they occupy. The methods developed provide new tools and avenues for studying and establishing effective countermeasures to risks associated with long-duration spaceflight.

PMID:40332907 | DOI:10.1167/iovs.66.5.17

Int J Mol Sci. 2025 Apr 20;26(8):3894. doi: 10.3390/ijms26083894.

ABSTRACT

Angelica biserrata (Shan et Yuan) Yuan et Shan (A. biserrata) roots, a widely distributed medicinal crop with intraspecific diversity, exhibits significant variability in coumarin content across habitats. This study integrated metabolomics and transcriptomics to dissect the spatial heterogeneity in metabolite profiles and gene expression, revealing the mechanisms driving coumarin biosynthesis divergence. By synthesizing climate-related big data with machine learning and Bayesian-optimized deep learning models, we identified key environmental drivers and predicted optimal cultivation conditions. The key findings were as follows: (1) differential regions most strongly influenced coumarin; (2) upstream genes (such as PAL-1, PAL-2, BGLU44, etc.) modulated downstream coumarin metabolites; (3) elevation (Elev) and warmest quarter temperature (Bio10) dominated coumarin variation, whereas May solar radiation (Srad5) and precipitation seasonality (Bio15) controlled transcriptomic reprogramming; (4) the optimized environment for bioactive compounds included mean annual temperature (Bio1) = 9.99 °C, annual precipitation (Bio12) = 1493 mm, Elev = 1728 m, cumulative solar radiation = 152,643 kJ·m-2·day-1, and soil organic carbon = 11,883 g·kg-1. This study aimed to clarify the biological characteristics and differential regulatory mechanisms of A. biserrata roots in different habitats, establish a theoretical framework for understanding the molecular mechanisms controlling metabolic changes under various habitats, and contribute to elucidating the formation of active constituents while facilitating their effective utilization.

PMID:40332784 | DOI:10.3390/ijms26083894

Med Biol Eng Comput. 2025 May 7. doi: 10.1007/s11517-025-03360-8. Online ahead of print.

ABSTRACT

Primary liver cancer is among the most lethal malignancies, with cell-level structural features such as polyploid giant cancer cells and mitotic figures strongly associated with poor patient prognosis. However, the quantification of these features is hindered by a shortage of pathologists, high workloads, and subjective discrepancies. To address these challenges, we leverage deep learning algorithms to enable the rapid detection of cell-level features, combining this capability with survival analysis to establish a novel, practical prognostic risk assessment system for liver cancer diagnosis and treatment. In collaboration with Peking University Shenzhen Hospital, we collected 172 liver cancer cases, comprising 340 pathology images, to construct the HCCP&M dataset. Our full-process calculation system integrates cell-level feature detection and survival analysis. During the detection phase, the CellFDet framework achieves F1 scores of 0.814, 0.819, and 0.935 for detecting polyploid giant cancer cells, mitotic figures, and general cells, respectively. In the survival analysis phase, patients were stratified into high-risk and low-risk groups based on the polyploid giant cancer cell index (P < 0.0001) and the mitotic index (P = 0.0025), with both indices demonstrating significant survival differences. Correlation analysis further confirmed these features as independent prognostic indicators for liver cancer. Our proposed system not only enables accurate detection of cell-level structural features but also provides reliable survival predictions, offering a valuable tool for improving the prognosis and treatment planning for liver cancer patients.

PMID:40332632 | DOI:10.1007/s11517-025-03360-8

Int J Mol Sci. 2025 Apr 13;26(8):3671. doi: 10.3390/ijms26083671.

ABSTRACT

AlphaFold3, the latest release of AlphaFold developed by Google DeepMind and Isomorphic Labs, was designed to predict protein structures with remarkable accuracy. AlphaFold3 enhances our ability to model not only single protein structures but also complex biomolecular interactions, including protein-protein interactions, protein-ligand docking, and protein-nucleic acid complexes. Herein, we provide a detailed examination of AlphaFold3's capabilities, emphasizing its applications across diverse biological fields and its effectiveness in complex biological systems. The strengths of the new AI model are also highlighted, including its ability to predict protein structures in dynamic systems, multi-chain assemblies, and complicated biomolecular complexes that were previously challenging to depict. We explore its role in advancing drug discovery, epitope prediction, and the study of disease-related mutations. Despite its significant improvements, the present review also addresses ongoing obstacles, particularly in modeling disordered regions, alternative protein folds, and multi-state conformations. The limitations and future directions of AlphaFold3 are discussed as well, with an emphasis on its potential integration with experimental techniques to further refine predictions. Lastly, the work underscores the transformative contribution of the new model to computational biology, providing new insights into molecular interactions and revolutionizing the fields of accelerated drug design and genomic research.

PMID:40332289 | DOI:10.3390/ijms26083671

Int J Mol Sci. 2025 Apr 13;26(8):3676. doi: 10.3390/ijms26083676.

ABSTRACT

The TONNEAU1 Recruited Motif (TRM) gene family is integral to the growth and development of various plants, playing a particularly critical role in regulating the shape of plant organs. While the functions of the TRM gene family in other plant species have been documented, knowledge regarding the members of the tomato (Solanum lycopersicum). SlTRM gene family remains limited, and their specific roles are not yet well understood. In this study, we identified and analyzed 28 members of the SlTRM gene family in tomato using bioinformatics approaches based on the latest whole genome data. Our analysis included the examination of protein structures, physicochemical properties, collinearity analysis, gene structures, conserved motifs, and promoter cis-acting elements of the SlTRM gene family members. The phylogenetic analysis indicated that both tomato and Arabidopsis thaliana are categorized into five distinct subfamilies. Furthermore, we conducted a three-dimensional structure prediction of 28 SlTRM genes for the first time, utilizing AlphaFold3, a deep learning architecture developed by DeepMind. Subsequently, we analyzed public transcriptome data to assess the tissue specificity of these 28 genes. Additionally, we examined the expression of SlTRM genes using RNA-seq and qRT-PCR techniques. Our analysis revealed that SlTRM25 was significantly differentially expressed, leading us to hypothesize that it may be involved in the development of lateral branches in tomatoes. Finally, we predicted the regulatory interaction network of SlTRM25 and identified that it interacts with genes such as SlFAF3/4b, SlCSR-like1, SlCSR-like2, and SlTRM19. This study serves as a reference for the investigation of the tomato TRM gene family members and introduces a novel perspective on the role of this gene family in the formation of lateral branches in tomatoes, offering both theoretical and practical significance.

PMID:40332175 | DOI:10.3390/ijms26083676

Int J Mol Sci. 2025 Apr 9;26(8):3525. doi: 10.3390/ijms26083525.

ABSTRACT

In the clinical setting, the efficacy of single-agent immune checkpoint inhibitors (ICIs) in triple-negative breast cancer (TNBC) remains suboptimal. Therefore, there is a pressing need to develop predictive biomarkers to identify non-responders. Considering that cancer-associated fibroblasts (CAFs) represent an integral component of the tumor microenvironment that affects the stiffness of solid tumors on shear-wave elastography (SWE) imaging, wound healing CAFs (WH CAFs) were identified in highly heterogeneous TNBC. This subtype highly expressed vitronectin (VTN) and constituted the majority of CAFs. Moreover, WH CAFs were negatively correlated with CD8+ T cell infiltration levels and influenced tumor proliferation in the Eo771 mouse model. Furthermore, multi-omics analysis validated its role in immunosuppression. In order to non-invasively classify patients as responders or non-responders to ICI monotherapy, a deep learning model was constructed to classify the level of WH CAFs based on SWE imaging. As anticipated, this model effectively distinguished the level of WH CAFs in tumors. Based on the classification of the level of WH CAFs, while tumors with a high level of WH CAFs were found to exhibit a poor response to anti programmed cell death protein 1 (PD-1) monotherapy, they were responsive to the combination of anti-PD-1 and erdafitinib, a selective fibroblast growth factor receptor (FGFR) inhibitor. Overall, these findings establish a reference for a novel non-invasive method for predicting ICI efficacy to guide the selection of TNBC patients for precision treatment in clinical settings.

PMID:40332007 | DOI:10.3390/ijms26083525

JMIR Med Inform. 2025 May 6;13:e76833. doi: 10.2196/76833.

NO ABSTRACT

PMID:40331761 | DOI:10.2196/76833

Technol Health Care. 2025 May;33(3):1169-1181. doi: 10.1177/09287329241300986. Epub 2024 Dec 25.

ABSTRACT

A brain tumor (BT) is considered one of the most crucial and deadly diseases in the world, as it affects the central nervous system and its main functions. Headaches, nausea, and balance problems are caused by tumors pressing on nearby brain tissue and affecting its function. The existing techniques are challenging to analyze diseased brain images since abnormal brain tissues lead to distorted or biased results during image processing, like tissue segmentation and non-rigid registration. To overcome these issues, proposed a DS-GAN model for inpainting brain MRI images. Initially, the input MRI images are segmented using a Gated shape convolution neural network (GS-CNN). In the first GAN, grayscale pixel intensities and the remaining image edges are utilized to create edge generators or edge reconstruction Generative Adversarial Networks (EGAN), which are capable of creating false edges in areas that are missing. The results of the experimental results demonstrated that the Jaccard Index (JI) was 0.82, while the Dice Index (DI) was 0.86. The proposed DS-GAN in terms of L1 loss, PSNR, SSIM, and MSE obtained was 2.18, 0.972, 32.04, and 26.42. As compared to existing techniques, the proposed DS-GAN model achieves an overall accuracy of 99.18%.

PMID:40331553 | DOI:10.1177/09287329241300986

Technol Health Care. 2025 May;33(3):1199-1220. doi: 10.1177/09287329241302558. Epub 2024 Dec 29.

ABSTRACT

BackgroundEffective brain tumour therapy and better patient outcomes depend on early tumour diagnosis. Accurate diagnosis can be hampered by traditional imaging techniques' frequent struggles with low resolution and noise, especially in Low Dose CT scans.Objectives: Through the integration of deep learning methods and sophisticated image processing techniques, this study seeks to establish a novel framework, the Multi Layered Chroma Edge Deep Net (MLCED-Net), to improve the accuracy of brain tumour diagnosis in Low Dose CT images.MethodsUsing the Lucy-Richardson technique for picture deblurring, Adaptive Histogram Equalisation, and pixel normalization to lower noise and enhance image quality are some of the pre-processing stages that are part of the suggested strategy. Main characteristics from the processed pictures are then retrieved, including mean, energy, contrast, and entropy. Following the feeding of these characteristics, the MLCED-Net model is used for classification and segmentation tasks. It utilises a 15-layer deep learning architecture.ResultsThe MLCED-Net model outperformed previous techniques by achieving an amazing accuracy rate of 98.9% in the detection of brain tumours. The suggested procedures were effective, as seen by the significant increases in image quality that the Peak Signal-to-Noise Ratio (PSNR) values showed after post-processing.Conclusions: A reliable method for brain tumour diagnosis in low-dose CT scans is offered by the MLCED-Net framework's combination of multi-layered autoencoders, color-based operations, and edge detection techniques. The present work underscores the capacity of sophisticated deep learning models to augment diagnostic precision, hence augmenting patient care and results.

PMID:40331540 | DOI:10.1177/09287329241302558

NMR Biomed. 2025 Jun;38(6):e70057. doi: 10.1002/nbm.70057.

ABSTRACT

Quantitative susceptibility mapping (QSM) provides the spatial distribution of magnetic susceptibility within tissues through sequential steps: phase unwrapping and echo combination, mask generation, background field removal, and dipole inversion. Accurate mask generation is crucial, as masks excluding regions outside the brain and without holes are necessary to minimize errors and streaking artifacts during QSM reconstruction. Variations in susceptibility values can arise from different mask generation methods, highlighting the importance of optimizing mask creation. In this study, we propose QSMmask-net, a deep neural network-based method for generating precise QSM masks. QSMmask-net achieved the highest Dice score compared to other mask generation methods. Mean susceptibility values using QSMmask-net masks showed the lowest differences from manual masks (ground truth) in simulations and healthy controls (no significant difference, p > 0.05). Linear regression analysis confirmed a strong correlation with manual masks for hemorrhagic lesions (slope = 0.9814 ± 0.007, intercept = 0.0031 ± 0.001, R2 = 0.9992, p < 0.05). We have demonstrated that mask generation methods can affect the susceptibility value estimations. QSMmask-net reduces the labor required for mask generation while providing mask quality comparable to manual methods. The proposed method enables users without specialized expertise to create optimized masks, potentially broadening QSM applicability efficiently.

PMID:40331503 | DOI:10.1002/nbm.70057

Int J Mol Sci. 2025 Apr 18;26(8):3850. doi: 10.3390/ijms26083850.

ABSTRACT

The resolution of the recent COVID-19 pandemic still requires attention, since the consequences of having suffered the infection, even in mild cases, are associated with several acute and chronic pathological conditions referred to as post-COVID syndrome (PCS). PCS often manifests with pulmonary disease and, in up to 9% of cases, a more serious complication known as post-COVID-19 pulmonary fibrosis (PC19-PF), which has a similar clinical course as idiopathic pulmonary fibrosis (IPF). Generating knowledge to provide robust evidence about the clinical benefits of different therapeutic strategies to treat the pulmonary effects of PCS can provide new insights to amplify therapeutic options for these patients. We present evidence found after a scoping review, following extended PRIMSA guidelines, for the use of immunomodulators in pulmonary PCS. We start with a brief description of the immunomodulatory properties of the relevant drugs, their clinically proven efficacy for viral infections and chronic inflammatory conditions, and their use during the COVID-19 pandemic. We emphasize the need for well-designed clinical trials to improve our understanding the physiopathology of pulmonary PCS and PC19-PF and also to determine the efficacy and safety of candidate treatments.

PMID:40332501 | DOI:10.3390/ijms26083850

Insects. 2025 Apr 17;16(4):422. doi: 10.3390/insects16040422.

ABSTRACT

The Neotropical brown stink bug (NBSB), Euschistus heros, is the most prevalent sucking soybean pest in Brazil, and control of it largely relies on the application of synthetic insecticides such as ethiprole, a phenylpyrazole insecticide targeting GABA-gated chloride channels encoded by the Rdl (resistant to dieldrin) gene. This study monitored 41 NBSB populations collected between 2021 and 2024 and revealed, for the first time, the presence of a mutation, A301S, in NBSB RDL receptors commonly known to confer target-site resistance to channel blockers such as phenylpyrazoles. Laboratory contact bioassays with ethiprole at 150 g a.i./ha (ethiprole label dose) revealed that most populations were quite susceptible, despite rather high resistance allele frequencies in some populations. Genotyping results confirmed that susceptible and A301S heterozygous genotypes largely dominate in frequency compared to homozygous resistant individuals, which exhibited high survivorship (84%) when exposed to discriminating rates of ethiprole in laboratory bioassays, while susceptible and heterozygote individuals showed lower survival rates (13% and 34%, respectively), suggesting an incompletely recessive trait conferring ethiprole resistance. Furthermore, we developed a TaqMan assay for molecular genotyping to monitor the spread of resistance allele frequency and to inform resistance management strategies for sustainable NBSB control using highly effective phenylpyrazole insecticides such as ethiprole.

PMID:40332961 | DOI:10.3390/insects16040422

Int J Mol Sci. 2025 Apr 15;26(8):3739. doi: 10.3390/ijms26083739.

ABSTRACT

As human life expectancy continues to rise, managing age-related diseases and preserving health in later years remain significant challenges. Consequently, there is a growing demand for strategies that enhance both the quality and the duration of life. Interventions that promote longevity, particularly those derived from natural sources, are popular for their potential to address age-related health concerns. Adaptogens-herbs, roots, and mushrooms-are valued in food science and nutrition for their ability to enhance resilience and overall well-being. Among these, Rhaponticum carthamoides (Willd.) Iljin, known as maral root (Russian leuzea), holds a prominent place in Siberian traditional medicine. The root extract, abundant in bioactive compounds such as flavonoids and phytoecdysteroids, is reputed for reducing fatigue, boosting strength, and offering immunomodulatory benefits. However, the effects of the plant extract on lifespan and age-related decline remains poorly studied. This study investigates the effect of maral root extract and phytoecdysteroids-ecdysterone, ponasterone, and turkesterone-on aging using Caenorhabditis elegans as a model organism. A sensitive liquid chromatography method with photodiode array detection was developed and validated to quantify the phytoecdysteroids in the extract. Behavioural and stress-response assays revealed that maral root not only extends lifespan but also significantly enhanced healthspan, stress resilience, and fitness in the nematodes. Additionally, treatment with ecdysterone, the most abundant compound in the root extract, improved healthspan by enhancing stress response. These findings underscore the potential of maral root as a natural adaptogen to mitigate age-related decline, providing valuable insights into natural longevity interventions.

PMID:40332350 | DOI:10.3390/ijms26083739

Acc Chem Res. 2025 May 7. doi: 10.1021/acs.accounts.5c00117. Online ahead of print.

ABSTRACT

ConspectusMethodological development in the fields of genetics, chemical biology, and biochemistry over the last several decades has provided researchers with a diverse set of powerful tools to investigate biological processes. Leveraging these innovations in concert, scientists can now characterize biological pathways at a level of complexity ranging from systems biology down to molecular and atomic detail.Throughout this Account, we illustrate how discoveries made using these tools build on each other to develop a comprehensive understanding of biological pathways. Advancements in genetic sequencing facilitates association of genotypes and phenotypes, independent of biochemical mechanism. Through the biochemical reconstitution of the interactions between biological macromolecules─including the small molecules (ligands and metabolites) and proteins─that participate in these biological pathways, scientists can characterize the specific molecular features that link genotype and phenotype. This facilitates identification of targets within these pathways that can be manipulated to achieve a greater understanding of the biological process or to develop interventions to improve human health outcomes.Specifically, we describe how this toolbox was leveraged to discover and characterize the molecular biochemistry underlying control of pathogenicity in the Gram-positive bacterium Staphylococcus aureus. Concurrent with advancements in the investigative tools available to the scientific community, we and others reported on the genetic, molecular, and biochemical/biophysical components of this regulatory system. Virulence control in S. aureus is achieved through a chemical system of bacterial cell-to-cell communication indexed to local population density, referred to as quorum sensing (QS). We and our collaborators identified that this QS system is encoded in the accessory gene regulator (agr) operon and functions via the biosynthesis, secretion, and accumulation of a short peptide signaling molecule─the autoinducing peptide (AIP)─in the local environment correlated with the growth of S. aureus in the same biological niche. Above a threshold concentration, these AIPs bind and activate a cell-surface receptor to stimulate an intracellular response resulting in altered gene expression and bacterial group behaviors. We discovered that chemical modification of these AIPs often generates molecules that exhibit potent inhibition of agr QS, with demonstrated therapeutic potential to treat S. aureus infections. We went on to characterize the biochemical mechanism of signaling molecule biosynthesis and receptor activation in controlled systems through in vitro reconstitution of the constituent enzymes and substrates. Biochemical reconstitution enabled quantitative assessment of biophysical parameters. These efforts culminated in the comprehensive characterization and functional in vitro reconstitution of agr QS in a synthetic system in a minimal model at the interface of genotype, mechanism, and phenotype.

PMID:40331756 | DOI:10.1021/acs.accounts.5c00117

Nucleic Acids Res. 2025 May 7:gkaf369. doi: 10.1093/nar/gkaf369. Online ahead of print.

ABSTRACT

Finding appropriate software and parameter settings to process shotgun metagenome data is essential for meaningful metagenomic analyses. To enable objective and comprehensive benchmarking of metagenomic software, the community-led initiative for the Critical Assessment of Metagenome Interpretation (CAMI) promotes standards and best practices. Since 2015, CAMI has provided comprehensive datasets, benchmarking guidelines, and challenges. However, benchmarking had to be conducted offline, requiring substantial time and technical expertise and leading to gaps in results between challenges. We introduce the CAMI Benchmarking Portal-a central repository of CAMI resources and web server for the evaluation and ranking of metagenome assembly, binning, and taxonomic profiling software. The portal simplifies evaluation, enabling users to easily compare their results with previous and other users' submissions through a variety of metrics and visualizations. As a demonstration, we benchmark software performance on the marine dataset of the CAMI II challenge. The portal currently hosts 28 675 results and is freely available at https://cami-challenge.org/.

PMID:40331433 | DOI:10.1093/nar/gkaf369

Front Aging Neurosci. 2025 Apr 22;17:1549072. doi: 10.3389/fnagi.2025.1549072. eCollection 2025.

ABSTRACT

OBJECTIVES: To investigate whether cerebrospinal fluid (CSF) circulation markers alter in patients with probable cerebral amyloid angiopathy (pCAA) and whether they are associated with brain degeneration and cognitive impairment.

METHODS: We screened pCAA patients from the ADNI3 database according to the Boston 2.0 Criteria. Fifty-two patients with cognitive impairment (26 pCAA; 26 age-sex-matched non-pCAA) and 26 age-sex-matched cognitively normal control (NC) were included in this study. All participants underwent neurological MRI and cognitive assessments. Choroid plexus (ChP) was segmented using a deep learning-based method and its volume was extracted. Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) was used to assess perivenous fluid mobility. AD pathological markers (Aβ and tau) were assessed using positron emission tomography. Brain parenchymal damage markers included white matter hyperintensities (WMH) volume and brain atrophy ratio. All markers were compared among the three groups. Correlations among the ChP volume, DTI-ALPS index, parenchymal damage markers, and cognitive scales were analyzed in the pCAA group.

RESULTS: The three groups exhibited significant differences in cognitive scores, AD biomarkers, and imaging markers. Post hoc analyses showed that patients with pCAA had significantly higher WMH volume, higher Aβ and tau deposition, and lower DTI-ALPS compared to NC. However, no difference in ChPs volume was found among the groups. Controlling for age, sex, and vascular risk factors, partial correlation analyses showed a significant negative correlation between the DTI-ALPS and WMH volume fraction (r = -0.606, p = 0.002). ChP volume was significantly associated with the Montreal cognitive assessment score (r = -0.492, p = 0.028).

CONCLUSION: CSF circulation markers were associated with elevated WMH burden and cognitive impairments in probable CAA.

PMID:40330595 | PMC:PMC12053238 | DOI:10.3389/fnagi.2025.1549072

Circ Res. 2025 May 7. doi: 10.1161/CIRCRESAHA.124.325699. Online ahead of print.

ABSTRACT

BACKGROUND: It has become evident that some women develop preeclampsia despite aspirin. This study aimed to examine how such aspirin nonresponsiveness develops in high-risk preeclampsia pregnancies by exploring the role of genetic polymorphisms and aspirin metabolism.

METHODS: The study involved pregnant women who developed preeclampsia despite low-dose aspirin and those who did not. First, we conducted a pharmacogenomic association study exploring the association of potential genetic variants with aspirin nonresponsiveness. Next, we analyzed the rate of enzymatic aspirin hydrolysis in maternal plasma. The extent of placental exposure to acetylsalicylic acid and its bioactive metabolites, that is, salicylic acid and gentisic acid, was determined by liquid chromatography-mass spectrometry. The expressions of AMEs (aspirin metabolizing enzymes), that is, GLYAT (glycine-N-acyltransferase), UGT1A6, CYP2E1, and NAT2 in the placenta, were analyzed by quantitative reverse transcription polymerase chain reaction, immunohistochemistry staining, and ELISA. Finally, the effects of AMEs were further examined on HTR-8/SVneo and human primary cytotrophoblast cells.

RESULT: Our genetic study showed that single-nucleotide polymorphisms (SNPs) of genes involved in aspirin pharmacokinetics and pharmacodynamics were not associated with aspirin nonresponsiveness in preeclampsia. Rates of aspirin hydrolysis in maternal plasma and the concentrations of acetylsalicylic acid, salicylic acid, and gentisic acid in the placenta did not differ between aspirin-responsive and aspirin-nonresponsive women. Intriguingly, GLYAT was significantly upregulated in the aspirin-nonresponsive placenta and associated with aspirin nonresponsiveness. This overexpression of GLYAT was found to diminish the proangiogenic, anti-inflammatory, and antisenescence effects of salicylic acid in HTR-8/SVneo and human primary cytotrophoblast cells.

CONCLUSIONS: Our study revealed that maternal genetic factors and plasma aspirin hydrolysis are not among the decisive factors in determining the effectiveness of low-dose aspirin in preventing preeclampsia among high-risk women. Instead, placental GLYAT appears to play a key role by limiting the effect of salicylic acid in the placenta.

PMID:40329906 | DOI:10.1161/CIRCRESAHA.124.325699

Acta Biochim Biophys Sin (Shanghai). 2025 May 6. doi: 10.3724/abbs.2025052. Online ahead of print.

ABSTRACT

Platinum drugs are widely used in lung cancer chemotherapy, but the immune characteristics of different individuals have different effects on the sensitivity and side effects of platinum drugs. In this study, we use 731 kinds of immune cell traits of 3757 healthy individuals and 429 patients with non-small cell lung cancer (NSCLC) in Xiangya Hospital of Central South University to conduct a Mendel randomized analysis in order to find out the causal relationship between some immune cell traits and the efficacy and adverse reactions of platinum drugs. We find that CD19 on CD24 +CD27 + B cell (OR = 0.598, P = 0.004) is the most significant immune cell trait as the protective factor of efficacy. HLA-DR +CD8 + T cell % lymphocyte (OR = 0.427, P = 7.55 × 10 -4) and HLA-DR +CD8 + T cell % T cell (OR = 0.471, P = 0.003) are the protective factors of liver injury. CD39 on CD39 + secreting CD4 + regulatory T cell (OR = 28.729, P = 0.009) and CD3 on CD39 + resting CD4 regulatory T cell (OR = 3.024, P = 0.009) are the risk factors of renal injury. Meanwhile, B cell-related traits mainly affect gastrointestinal upset and cutaneous toxicity, while T cell-related traits mainly affect other outcome variables. These findings may promote our understanding of the relationship between the efficacy and adverse reactions of platinum drugs and the immune system, and promote future development of biomarkers for predicting the efficacy and adverse reactions of platinum drugs.

PMID:40329806 | DOI:10.3724/abbs.2025052

Front Immunol. 2025 Apr 22;16:1576141. doi: 10.3389/fimmu.2025.1576141. eCollection 2025.

ABSTRACT

In recent years, the study of the interaction between gut microbiota and distant organs such as the heart, lungs, brain, and liver has become a hot topic in the field of gut microbiology. With a deeper understanding of its immune regulation and mechanisms of action, these findings have increasingly highlighted their guiding value in clinical practice. The gut is not only the largest digestive organ in the human body but also the habitat for most microorganisms. Imbalances in gut microbial communities have been associated with various lung diseases, such as allergic asthma and cystic fibrosis. Furthermore, gut microbial communities have significant impacts on metabolic function and immune responses. Their metabolites not only regulate gastrointestinal immune systems but may also affect distant organs such as the lungs and brain. As one of the most common types of respiratory system diseases worldwide, pulmonary infections have high morbidity and mortality rates. Pulmonary infections caused by immune dysfunction can lead to gastrointestinal problems like diarrhea, further resulting in imbalances within complex interactions that are associated with abnormal manifestations under disequilibrium conditions. Meanwhile, clinical interventions can significantly modulate the composition of gut microbiota, and alteration in gut microbiota may subsequently indicate susceptibility to pulmonary infections and even contribute to the prevention or regulation of their progression. This review delves into the interaction between gut microbiota and pulmonary infections, elucidating the latest advancements in gut-lung axis research and providing a fresh perspective for the treatment and prevention of pneumonia.

PMID:40330490 | PMC:PMC12052896 | DOI:10.3389/fimmu.2025.1576141

Int J Cardiol Congenit Heart Dis. 2025 Apr 15;20:100584. doi: 10.1016/j.ijcchd.2025.100584. eCollection 2025 Jun.

ABSTRACT

BACKGROUND: High-throughput proteomics platforms using aptamers (SomaScan) or proximity extension assay (Olink) provide novel opportunities for improving diagnostic and risk stratification tools in cardiovascular diseases, including understudied congenital heart diseases. The correlation between these proteomics approaches has not yet been studied among individuals with a Fontan circulation.

OBJECTIVE: The correlation of plasma protein measurements between SomaScan and Olink platforms was evaluated in adults with a Fontan circulation.

METHODS: We measured 491 proteins in plasma of 71 adults with a Fontan circulation using Olink and SomaScan. Missing Olink measurements (0.13%, 47/34,861) were imputed using non-parametric imputation. Spearman's rank correlation coefficient for absolute values of protein expression between platforms was calculated. Protein correlation frequencies were compared to 3 cohorts reported in the literature using Pearson's Chi-squared test of independence.

RESULTS: Overall, protein correlations between Olink and SomaScan measurements were moderately strong for most proteins, (rho > 0.4 for 57.2%), but with substantial variability (median correlation = 0.457, IQR = 0.538). The distribution of protein correlations was qualitatively similar to published literature in non-Fontan cohorts. Both Olink and SomaScan identified proteins with sex-based differences; both identified differences in myostatin and leptin, but each identified additional nonoverlapping sexually dimorphic proteins (n = 14 Olink, n = 5 SomaScan).

CONCLUSIONS: In adults with a Fontan circulation, correlations between plasma proteins measured by Olink and SomaScan varied widely, approximately in line with prior reports in other populations. While these tools may be uniquely useful to generate hypotheses, specifically regarding potential molecular mechanisms, more definitive inference requires independent validation.

PMID:40330320 | PMC:PMC12053979 | DOI:10.1016/j.ijcchd.2025.100584