Eur Heart J Digit Health. 2025 Jan 28;6(2):270-284. doi: 10.1093/ehjdh/ztaf005. eCollection 2025 Mar.

ABSTRACT

Intracoronary optical coherence tomography (OCT) is a valuable tool for, among others, periprocedural guidance of percutaneous coronary revascularization and the assessment of stent failure. However, manual OCT image interpretation is challenging and time-consuming, which limits widespread clinical adoption. Automated analysis of OCT frames using artificial intelligence (AI) offers a potential solution. For example, AI can be employed for automated OCT image interpretation, plaque quantification, and clinical event prediction. Many AI models for these purposes have been proposed in recent years. However, these models have not been systematically evaluated in terms of model characteristics, performances, and bias. We performed a systematic review of AI models developed for OCT analysis to evaluate the trends and performances, including a systematic evaluation of potential sources of bias in model development and evaluation.

PMID:40110224 | PMC:PMC11914731 | DOI:10.1093/ehjdh/ztaf005

Eur Heart J Digit Health. 2025 Feb 25;6(2):170-179. doi: 10.1093/ehjdh/ztae088. eCollection 2025 Mar.

ABSTRACT

AIMS: Sudden cardiac arrest (SCA) is a commonly fatal event that often occurs without prior indications. To improve outcomes and enable preventative strategies, the electrocardiogram (ECG) in conjunction with deep learning was explored as a potential screening tool.

METHODS AND RESULTS: A publicly available data set containing 10 s of 12-lead ECGs from individuals who did and did not have an SCA, information about time from ECG to arrest, and age and sex was utilized for analysis to individually predict SCA or not using deep convolution neural network models. The base model that included age and sex, ECGs within 1 day prior to arrest, and data sampled from windows of 720 ms around the R-waves from 221 individuals with SCA and 1046 controls had an area under the receiver operating characteristic curve of 0.77. With sensitivity set at 95%, base model specificity was 31%, which is not clinically applicable. Gradient-weighted class activation mapping showed that the model mostly relied on the QRS complex to make predictions. However, models with ECGs recorded between 1 day to 1 month and 1 month to 1 year prior to arrest demonstrated predictive capabilities.

CONCLUSION: Deep learning models processing ECG data are a promising means of screening for SCA, and this method explains differences in SCAs due to age and sex. Model performance improved when ECGs were nearer in time to SCAs, although ECG data up to a year prior had predictive value. Sudden cardiac arrest prediction was more dependent upon QRS complex data compared to other ECG segments.

PMID:40110219 | PMC:PMC11914729 | DOI:10.1093/ehjdh/ztae088

Eur Heart J Digit Health. 2025 Jan 16;6(2):209-217. doi: 10.1093/ehjdh/ztae097. eCollection 2025 Mar.

ABSTRACT

AIMS: Delayed diagnosis of pulmonary hypertension (PH) is a known cause of poor patient prognosis. We aimed to develop an artificial intelligence (AI) model, using ensemble learning method to detect PH using electrocardiography (ECG), chest X-ray (CXR), and brain natriuretic peptide (BNP), facilitating accurate detection and prompting further examinations.

METHODS AND RESULTS: We developed a convolutional neural network model using ECG data to predict PH, labelled by ECG from seven institutions. Logistic regression was used for the BNP prediction model. We referenced a CXR deep learning model using ResNet18. Outputs from each of the three models were integrated into a three-layer fully connected multimodal model. Ten cardiologists participated in an interpretation test, detecting PH from patients' ECG, CXR, and BNP data both with and without the ensemble learning model. The area under the receiver operating characteristic curves of the ECG, CXR, BNP, and ensemble learning model were 0.818 [95% confidence interval (CI), 0.808-0.828], 0.823 (95% CI, 0.780-0.866), 0.724 (95% CI, 0.668-0.780), and 0.872 (95% CI, 0.829-0.915). Cardiologists' average accuracy rates were 65.0 ± 4.7% for test without AI model and 74.0 ± 2.7% for test with AI model, a statistically significant improvement (P < 0.01).

CONCLUSION: Our ensemble learning model improved doctors' accuracy in detecting PH from ECG, CXR, and BNP examinations. This suggests that earlier and more accurate PH diagnosis is possible, potentially improving patient prognosis.

PMID:40110214 | PMC:PMC11914732 | DOI:10.1093/ehjdh/ztae097

Front Physiol. 2025 Mar 4;16:1518732. doi: 10.3389/fphys.2025.1518732. eCollection 2025.

ABSTRACT

INTRODUCTION: Understanding the hemodynamics of blood circulation is crucial to reveal the processes contributing to stenosis and atherosclerosis development.

METHOD: Computational fluid dynamics (CFD) facilitates this understanding by simulating blood flow patterns in coronary arteries. Nevertheless, applying CFD in fast-response scenarios presents challenge due to the high computational costs. To overcome this challenge, we integrate a deep learning (DL) method to improve efficiency and responsiveness. This study presents a DL approach for predicting Time-Averaged Wall Shear Stress (TAWSS) values in coronary arteries' bifurcation.

RESULTS: To prepare the dataset, 1800 idealized models with varying morphological parameters are created. Afterward, we design a CNN-based U-net architecture to predict TAWSS by the point cloud of the geometries. Moreover, this architecture is implemented using TensorFlow 2.3.0. Our results indicate that the proposed algorithms can generate results in less than one second, showcasing their suitability for applications in terms of computational efficiency.

DISCUSSION: Furthermore, the DL-based predictions demonstrate strong agreement with results from CFD simulations, with a normalized mean absolute error of only 2.53% across various cases.

PMID:40110184 | PMC:PMC11920710 | DOI:10.3389/fphys.2025.1518732

Front Artif Intell. 2025 Mar 5;8:1546398. doi: 10.3389/frai.2025.1546398. eCollection 2025.

ABSTRACT

Predicting Gross Domestic Product (GDP) is one of the most crucial tasks in analyzing a nation's economy and growth. The primary goal of this study is to forecast GDP using factors such as government spending, inflation, official development aid, remittance inflows, and Foreign Direct Investment (FDI). Additionally, the paper aims to provide an alternative perspective to Generative Adversarial Networks method and demonstrate how such deep learning technique can enhance the accuracy of GDP predictions with small data and economy like The Gambia. We proposed the implementation of Generative Adversarial Networks to predict GDP using various economic factors over the period from 1970 to 2022. Performance metrics, including the coefficient of determination R2, mean absolute error (MAE), mean absolute percentage error (MAPE), and root- mean-square error (RMSE) were collected to evaluate the system's accuracy. Among the models tested-Random Forest Regression (RF), XGBoost (XGB), and Support Vector Regression (SVR)-the Generative Adversarial Networks (GAN) model demonstrated superior performance, achieving the highest accuracy, which is 99% prediction accuracies. The most dependable model for capturing intricate correlations between GDP and its affecting components, however, RF and XGBoost, also achieved an accuracy of 98% each. This makes GAN the most desirable model for GDP prediction for our study. Through data analysis, this project aims to provide actionable insights to support strategies that sustain economic boom. This approach enables the generation of accurate GDP forecasts, offering a valuable tool for policymakers and stakeholders.

PMID:40110175 | PMC:PMC11920123 | DOI:10.3389/frai.2025.1546398

Front Res Metr Anal. 2025 Mar 5;10:1509502. doi: 10.3389/frma.2025.1509502. eCollection 2025.

ABSTRACT

OBJECTIVE: Literature-based Discovery (LBD) identifies new knowledge by leveraging existing literature. It exploits interconnecting implicit relationships to build bridges between isolated sets of non-interacting literatures. It has been used to facilitate drug repurposing, new drug discovery, and study adverse event reactions. Within the last decade, LBD systems have transitioned from using statistical methods to exploring deep learning (DL) to analyze semantic spaces between non-interacting literatures. Recent works explore knowledge graphs (KG) to represent explicit relationships. These works envision LBD as a knowledge graph completion (KGC) task and use DL to generate implicit relationships. However, these systems require the researcher to have domain-expert knowledge when submitting relevant queries for novel hypothesis discovery.

METHODS: Our method explores a novel approach to identify all implicit hypotheses given the researcher's search query and expedites the knowledge discovery process. We revise the KGC task as the task of predicting interconnecting vertex embeddings within the graph. We train our model using a similarity learning objective and compare our model's predictions against all known vertices within the graph to determine the likelihood of an implicit relationship (i.e., connecting edge). We also explore three approaches to represent edge connections between vertices within the KG: average, concatenation, and Hadamard. Lastly, we explore an approach to induce inductive biases and expedite model convergence (i.e., input representation scaling).

RESULTS: We evaluate our method by replicating five known discoveries within the Hallmark of Cancer (HOC) datasets and compare our method to two existing works. Our results show no significant difference in reported ranks and model convergence rate when comparing scaling our input representations and not using this method. Comparing our method to previous works, we found our method achieves optimal performance on two of five datasets and achieves comparable performance on the remaining datasets. We further analyze our results using statistical significance testing to demonstrate the efficacy of our method.

CONCLUSION: We found our similarity-based learning objective predicts linking vertex embeddings for single relationship closed discovery replication. Our method also provides a ranked list of linking vertices between a set of inputs. This approach reduces researcher burden and allows further exploration of generated hypotheses.

PMID:40110121 | PMC:PMC11920161 | DOI:10.3389/frma.2025.1509502

ACS Med Chem Lett. 2025 Feb 10;16(3):410-419. doi: 10.1021/acsmedchemlett.4c00511. eCollection 2025 Mar 13.

ABSTRACT

AXL, part of the TAM receptor tyrosine kinase family, plays a significant role in the growth and survival of various tissues and tumors, making it a critical target for cancer therapy. This study introduces a novel high-throughput virtual screening (HTVS) methodology that merges an AI-enhanced graph neural network, PLANET, with a geometric deep learning algorithm, DeepDock. Using this approach, we identified potent AXL inhibitors from our database. Notably, compound 9, with an IC50 of 9.378 nM, showed excellent inhibitory activity, suggesting its potential as a candidate for further research. We also performed molecular dynamics simulations to explore the interactions between compound 9 and AXL, providing insights for future enhancements. This hybrid screening method proves effective in finding promising AXL inhibitors, and advancing the development of new cancer therapies.

PMID:40110119 | PMC:PMC11921171 | DOI:10.1021/acsmedchemlett.4c00511

Aging Biol. 2024;2:e20240023. doi: 10.59368/agingbio.20240023. Epub 2024 Feb 13.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is an age-associated lung disease of unknown etiology that is characterized by exaggerated deposition of extracellular matrix (ECM), leading to distorted lung architecture, respiratory failure, and death. There are no truly effective treatment options for IPF, thus highlighting the importance of exploring new pathogenic mechanisms that underlie the development of fibrosis and of identifying new therapeutic targets. Insulin-like growth factors (IGFs) are known to be pro-fibrotic. However, the ubiquity and essentiality of IGF receptor signaling in normal physiology limit its potential as a direct therapeutic target. In a recent study, we found a highly significant correlation between expression of pregnancy-associated plasma protein (PAPP)-A in human IPF lung tissue and disease severity. PAPP-A is a unique metalloprotease that enhances local IGF action. In vitro studies support a role for proteolytically active PAPP-A in promoting a fibrotic phenotype in adult human lung fibroblasts. Here, we show that PAPP-A is preferentially expressed in mouse lung fibroblasts and that inhibition of PAPP-A in vivo through PAPP-A gene deletion or a specific neutralizing monoclonal antibody against PAPP-A markedly reduced the progression of bleomycin-induced lung fibrosis, as measured by significantly decreased ECM expression and improved lung histology. Surrogate markers of local IGF receptor activity in the lung were also significantly reduced, indicating indirect modulation of IGF signaling through PAPP-A. These results establish a role for PAPP-A in pulmonary fibrosis and point to PAPP-A as a selective and pharmacologically tractable target for IPF and possibly other fibrotic disorders.

PMID:40110162 | PMC:PMC11922547 | DOI:10.59368/agingbio.20240023

Front Pharmacol. 2025 Mar 5;16:1524505. doi: 10.3389/fphar.2025.1524505. eCollection 2025.

ABSTRACT

OBJECTIVE: This systematic review and meta-analysis aims to assess the efficacy of GBE in the treatment of IPF by evaluating its impact on total effective rate, blood gas analysis, pulmonary function tests, and markers of inflammation and fibrosis.

METHODS: We conducted a comprehensive search across seven databases, including PubMed, EMBASE, Web of Science, CNKI, Wanfang DATA, VIP, and CBM, without restrictions on publication date. Randomized controlled trials (RCTs) that investigated the effects of GBE on IPF patients were eligible for inclusion. Relevant literature was screened, and the data in the included studies were extracted for quality assessment according to the Risk of bias tool.

RESULTS: A total of 14 RCTs involving 1043 patients were included in the analysis. GBE significantly improved the total effective rate, arterial oxygen partial pressure, arterial oxygen saturation, forced vital capacity, forced expiratory volume in one second, maximum voluntary ventilation, and 6-min walk test compared to the control group. Additionally, there was a significant reduction in arterial carbon dioxide partial pressure, interleukin-4, hyaluronan, and laminin levels.

CONCLUSION: GBE may offer therapeutic benefits in IPF by improving respiratory function, modulating inflammation, and affecting fibrosis markers. These findings support the potential use of GBE as an adjunct therapy in IPF and suggest that further large-scale, multicenter trials are warranted to confirm its efficacy and safety.

PMID:40110130 | PMC:PMC11919911 | DOI:10.3389/fphar.2025.1524505

Clin Transplant. 2025 Mar;39(3):e70136. doi: 10.1111/ctr.70136.

ABSTRACT

BACKGROUND: Lung transplantation (LT) is a critical option for patients with advanced respiratory diseases, especially interstitial lung diseases (ILD). The GAP score (Gender, Age, Physiology) has shown prognostic value in idiopathic pulmonary fibrosis (IPF), but its utility in other progressive fibrotic diseases and LT candidates is less well-studied.

METHODS: This retrospective study included ILD patients evaluated as LT candidates between January 2017 and December 2023 at a single center. The GAP score was calculated for each patient, and patients were classified into GAP stages I, II, or III. Outcomes evaluated included LT waiting list inclusion, LT performed, death, and active follow-up without waiting list inclusion. The prognostic utility was analyzed using survival analysis, including Cox regression and Kaplan-Meier methods.

RESULTS: Of 413 ILD patients, 119 were included on the LT waiting list. GAP stage III was an independent predictor of transplant-free survival (HR = 2.720; p = 0.011). Patients in stage II showed a transplant-free survival of 51.3% at 2 years, while stage III had 49.2% survival at 1 year. GAP stages significantly predicted transplant outcomes and survival rates (p < 0.001).

CONCLUSION: The GAP score is a reliable prognostic tool for ILD patients being evaluated for LT, aiding in decision-making regarding referral and waiting list inclusion. It may serve as a useful marker for early referral and prioritization.

PMID:40109152 | DOI:10.1111/ctr.70136

Nature. 2025 Mar 19. doi: 10.1038/s41586-025-08727-3. Online ahead of print.

ABSTRACT

Fibrosis, the replacement of healthy tissue with collagen-rich matrix, can occur following injury in almost every organ1,2. Mouse lungs follow a stereotyped sequence of fibrogenesis-to-resolution after bleomycin injury3, and we reasoned that profiling post-injury histological stages could uncover pro-fibrotic versus anti-fibrotic features with functional value for human fibrosis. Here we quantified spatiotemporally resolved matrix transformations for integration with multi-omic data. First, we charted stepwise trajectories of matrix aberration versus resolution, derived from a high-dimensional set of histological fibre features, that denoted a reversible transition in uniform-to-disordered histological architecture. Single-cell sequencing along these trajectories identified temporally enriched 'ECM-secreting' (Csmd1-expressing) and 'pro-resolving' (Cd248-expressing) fibroblasts at the respective post-injury stages. Visium-based spatial analysis further suggested divergent matrix architectures and spatial-transcriptional neighbourhoods by fibroblast subtype, identifying distinct fibrotic versus non-fibrotic biomolecular milieu. Critically, pro-resolving fibroblast instillation helped to ameliorate fibrosis in vivo. Furthermore, the fibroblast neighbourhood-associated factors SERPINE2 and PI16 functionally modulated human lung fibrosis ex vivo. Spatial phenotyping of idiopathic pulmonary fibrosis at protein level additionally uncovered analogous fibroblast subtypes and neighbourhoods in human disease. Collectively, these findings establish an atlas of pro- and anti-fibrotic factors that underlie lung matrix architecture and implicate fibroblast-associated biological features in modulating fibrotic progression versus resolution.

PMID:40108456 | DOI:10.1038/s41586-025-08727-3

Ecol Evol. 2025 Mar 18;15(3):e71119. doi: 10.1002/ece3.71119. eCollection 2025 Mar.

ABSTRACT

Sexual differences in physiology are widely regarded as potential proximate mechanisms that underlie sex differences in mortality, life history and disease risk of vertebrates. However, little is known about the causes of sex-specific variation in physiology. Sexual selection and parental workload are two key components suggested to play a role. Theory predicts that, within males, species with stronger male sexual selection (greater sexual dichromatism and more frequent social polygyny) and higher male parental effort should have lower immune capacity and stronger oxidative imbalance. Within females, a weak or no direct effect of male sexual selection on physiology is expected, but species where females invest more in parental care should have lower immune capacity and higher oxidative imbalance. We tested these predictions by phylogenetic comparative analyses conducted separately for the two sexes and based on 11,586 physiological measurements of samples collected in the field from 2048 individuals of 116 and 106 European bird species for males and females, respectively. For males, we found that the degree of dichromatism, polygyny and male parental effort correlated negatively with multiple immune indices, and the level of antioxidant glutathione correlated positively with polygyny score. In contrast, female immune and oxidative variables were unrelated or weakly related to both male sexual selection and female parental effort. We conclude that sex roles can drive inter-specific variation in immune function (primarily in male birds), but less so in oxidative physiology. These findings support earlier claims that males pay higher physiological costs of sexual selection than females, but apparently also of caregiving. We discuss how females might avoid such costs.

PMID:40109553 | PMC:PMC11919744 | DOI:10.1002/ece3.71119

Comput Struct Biotechnol J. 2025 Feb 27;27:804-812. doi: 10.1016/j.csbj.2025.02.029. eCollection 2025.

ABSTRACT

N7-methylguanosine (m7G) modifications play a pivotal role in RNA stability, mRNA export, and protein translation. They are closely associated with ribosome function and the regulation of gene expression. Dysregulation of m7G has been implicated in various diseases, including cancers and neurodegenerative disorders, where the loss of m7G can lead to genomic instability and uncontrolled cell proliferation. Accurate identification of m7G sites is thus essential for elucidating these mechanisms. Due to the high cost of experimentally validating m7G sites, several artificial intelligence models have been developed to predict these sites. However, the performance of these models is not yet optimal, and a user-friendly web server is still needed. To address these issues, we developed CAP-m7G, an innovative model that integrates Chaos Game Representation, Capsule Networks, and reconstruction layers. CAP-m7G achieved an accuracy of 96.63%, a specificity of 95.07%, and a Matthews correlation coefficient (MCC) of 0.933 on independent test data. Our results demonstrate that the integration of Chaos Game Representation with Capsule Network can effectively capture the crucial sequence information associated with m7G sites. The web server can be accessed at https://awi.cuhk.edu.cn/~biosequence/CAP-m7G/index.php.

PMID:40109445 | PMC:PMC11919597 | DOI:10.1016/j.csbj.2025.02.029

Lab Chip. 2025 Mar 20. doi: 10.1039/d5lc00015g. Online ahead of print.

ABSTRACT

Glioblastoma is one of the most malignant tumors in the world, but the development of its therapies remains limited. Herein, a microfluidic chip that mimics the cerebrospinal fluid (CSF) circulation microenvironment is proposed to study the migration characteristics of glioblastoma U87-MG cells and U251 cells in complex environments where glioblastoma coexists with diseases that elevate CSF levels. In the presence of interstitial flow (IF), changing both cell densities and the cellular environment results in increased cell motility, including an increase in the number of migrating cells, the mean displacement of the top 30% fastest-moving cells, and the overall mean displacement. Then, through dynamic migration characterization analysis, it was found that IF enhances cell velocity and speed. Importantly, cells exposed to IF tend to migrate in directions with smaller angles of deviation from the opposite direction of IF. Finally, cytoskeleton inhibitors and decreased expressions of focal adhesion proteins, such as cytochalasin D, FAK inhibitors (VS-6063 and PF-573228), and FAK siRNA, were both proved to decrease the cells' response to IF. This work not only demonstrates the effect of IF on glioblastoma cell migration, but also indicates the reliability of microfluidic chips for modeling complex physiological environments, which is expected to be further developed for drug screening.

PMID:40109161 | DOI:10.1039/d5lc00015g

J Phys Chem B. 2025 Mar 20. doi: 10.1021/acs.jpcb.4c07325. Online ahead of print.

ABSTRACT

Insulin fibrillation inflicts both economic and clinical challenges by causing bioactivity loss, inflammation, and adverse effects during storage, transport, and injection. The present study explores antiamyloidogenic and fibril-disaggregating effects of a gut microbiota-derived indole metabolite, indole-3-acetic acid (IAA) on insulin fibrillation. According to Thioflavin T (ThT) fluorescence assays and transmission electron microscopy (TEM), IAA significantly inhibited both primary and seed-induced fibrillation of insulin. We note that IAA reduced insulin aggregate sizes as evident from the scattering profiles, while circular dichroism studies confirmed that IAA preserves native α-helical structure possibly minimizing the exposed surface hydrophobicity of insulin. Additionally, IAA showed effectiveness in breaking apart preformed fibrils, indicated by a time-dependent decrease in ThT fluorescence and further confirmed by TEM. Our biolayer interferometry interaction studies revealed a moderate 2:1 binding affinity between IAA and insulin. Two key binding sites on insulin were identified via machine-learning-based-docking and hybrid QM/MM studies, where IAA interacts. Site I (Leu13A, Tyr14A, Glu17A, Phe1B) showed more favorable interaction energetics than site II (Tyr19A, Phe25B, Thr27B) based on SAPT0 residue-wise interaction energy analysis. IAA also protected cells from fibril-induced cytotoxicity and hemolysis, thereby offering a promising therapeutic option for amyloid-related disorders, with dual action in preventing fibril formation and promoting fibril disaggregation.

PMID:40109067 | DOI:10.1021/acs.jpcb.4c07325

Plant Cell Environ. 2025 Mar 19. doi: 10.1111/pce.15456. Online ahead of print.

ABSTRACT

Bacteriophages impact soil bacteria through lysis, altering the availability of organic carbon and plant nutrients. However, the magnitude of nutrient uptake by plants from lysed bacteria remains unknown, partly because this process is challenging to investigate in the field. In this study, we extend ecosystem fabrication (EcoFAB 2.0) approaches to study plant-bacteria-phage interactions by comparing the impact of virocell (phage-lysed) and uninfected 15N-labelled bacterial necromass on plant nitrogen acquisition and rhizosphere exometabolites composition. We show that grass Brachypodium distachyon derives some nitrogen from amino acids in uninfected Pseudomonas putida necromass lysed by sonication but not from virocell necromass. Additionally, the bacterial necromass elicits the formation of rhizosphere exometabolites, some of which (guanosine), alongside tested aromatic acids (p-coumaric and benzoic acid), show bacterium-specific effects on bacteriophage-induced lysis when tested in vitro. The study highlights the dynamic feedback between virocell necromass and plants and suggests that root exudate metabolites can impact bacteriophage infection dynamics.

PMID:40108761 | DOI:10.1111/pce.15456

Microbiome. 2025 Mar 19;13(1):76. doi: 10.1186/s40168-025-02066-1.

ABSTRACT

BACKGROUND: Female genital tract (FGT) diseases such as bacterial vaginosis (BV) and sexually transmitted infections are prevalent in South Africa, with young women being at an increased risk. Since imbalances in the FGT microbiome are associated with FGT diseases, it is vital to investigate the factors that influence FGT health. The mycobiome plays an important role in regulating mucosal health, especially when the bacterial component is disturbed. However, we have a limited understanding of the FGT mycobiome since many studies have focused on bacterial communities and have neglected low-abundance taxonomic groups, such as fungi. To reduce this knowledge deficit, we present the first large-scale metaproteomic study to define the taxonomic composition and potential functional processes of the FGT mycobiome in South African reproductive-age women.

RESULTS: We examined FGT fungal communities present in 123 women by collecting lateral vaginal wall swabs for liquid chromatography-tandem mass spectrometry. From this, 39 different fungal genera were identified, with Candida dominating the mycobiome (53.2% relative abundance). We observed changes in relative abundance at the protein, genus, and functional (gene ontology biological processes) level between BV states. In women with BV, Malassezia and Conidiobolus proteins were more abundant, while Candida proteins were less abundant compared to BV-negative women. Correspondingly, Nugent scores were negatively associated with total fungal protein abundance. The clinical variables, Nugent score, pro-inflammatory cytokines, chemokines, vaginal pH, Chlamydia trachomatis, and the presence of clue cells were associated with fungal community composition.

CONCLUSIONS: The results of this study revealed the diversity of FGT fungal communities, setting the groundwork for understanding the FGT mycobiome. Video Abstract.

PMID:40108637 | DOI:10.1186/s40168-025-02066-1

Cell Death Differ. 2025 Mar 19. doi: 10.1038/s41418-025-01476-w. Online ahead of print.

ABSTRACT

Mammalian mitochondria undergo Ca2+-induced and cyclosporinA (CsA)-regulated permeability transition (mPT) by activating the mitochondrial permeability transition pore (mPTP) situated in mitochondrial inner membranes. Ca2+-induced prolonged openings of mPTP under certain pathological conditions result in mitochondrial swelling and rupture of the outer membrane, leading to mitochondrial dysfunction and cell death. While the exact molecular composition and structure of mPTP remain unknown, mammalian ATP synthase was reported to form voltage and Ca2+-activated leak channels involved in mPT. Unlike in mammals, mitochondria of the crustacean Artemia franciscana have the ability to accumulate large amounts of Ca2+ without undergoing the mPT. Here, we performed structural and functional analysis of A. franciscana ATP synthase to study the molecular mechanism of mPTP inhibition in this organism. We found that the channel formed by the A. franciscana ATP synthase dwells predominantly in its inactive state and is insensitive to Ca2+, in contrast to porcine heart ATP synthase. Single-particle cryo-electron microscopy (cryo-EM) analysis revealed distinct structural features in A. franciscana ATP synthase compared with mammals. The stronger density of the e-subunit C-terminal region and its enhanced interaction with the c-ring were found in A. franciscana ATP synthase. These data suggest an inactivation mechanism of the ATP synthase leak channel and its possible contribution to the lack of mPT in this organism.

PMID:40108410 | DOI:10.1038/s41418-025-01476-w

EMBO Rep. 2025 Mar 19. doi: 10.1038/s44319-025-00400-0. Online ahead of print.

ABSTRACT

Small open-reading frame-encoded micropeptides within long noncoding RNAs (lncRNAs) are often overlooked due to their small size and low abundance. However, emerging evidence links these micropeptides to various biological pathways, though their roles in neural development and neurodegeneration remain unclear. Here, we investigate the function of murine micropeptide Sertm2, encoded by the lncRNA A730046J19Rik, during spinal motor neuron (MN) development. Sertm2 is predicted to be a conserved transmembrane protein found in both mouse and human, with subcellular analysis revealing that it is enriched in the cytoplasm and neurites. By generating C terminally Flag-tagged Sertm2 and expressing it from the A730046J19Rik locus, we demonstrate that the Sertm2 micropeptide localizes in spinal MNs in mice. The GDNF signaling-induced Etv4+ motor pool is impaired in Sertm2 knockout mice, which display motor nerve arborization defects that culminate in impaired motor coordination and muscle weakness. Similarly, human SERTM2 knockout iPSC-derived MNs also display reduced ETV4+ motor pools, highlighting that Sertm2 is a novel, evolutionarily conserved micropeptide essential for maintaining GDNF-induced MN subtype identity.

PMID:40108406 | DOI:10.1038/s44319-025-00400-0

Sci Rep. 2025 Mar 19;15(1):9415. doi: 10.1038/s41598-025-94362-x.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers known to humans. However, not all patients fare equally poor survival, and a minority of patients even survives advanced disease for months or years. Thus, there is a clinical need to search corresponding prognostic biomarkers which forecast survival on an individual basis. To dig more information and identify potential biomarkers from PDAC pathological slides, we trained a deep learning (DL) model based U-net-shaped backbone. This DL model can automatically detect tumor, stroma and lymphocytes on whole slide images (WSIs) of PDAC patients. We performed an analysis of 800 PDAC scans, categorizing stroma in percentage (SIP) and lymphocytes in percentage (LIP) into two and three categories, respectively. The presented model achieved remarkable accuracy results with a total accuracy of 94.72%, a mean intersection of union rate of 78.66%, and a mean dice coefficient of 87.74%. Survival analysis revealed that SIP-mediate and LIP-high groups correlated with enhanced median overall survival (OS) across all cohorts. These findings underscore the potential of SIP and LIP as prognostic biomarkers for PDAC and highlight the utility of DL as a tool for PDAC biomarkers detecting on WSIs.

PMID:40108402 | DOI:10.1038/s41598-025-94362-x