Radiol Imaging Cancer. 2025 Mar;7(2):e240213. doi: 10.1148/rycan.240213.

ABSTRACT

Purpose To develop deep learning (DL) radiopathomics models based on contrast-enhanced MRI and pathologic imaging to predict vessels encapsulating tumor clusters (VETC) and survival in hepatocellular carcinoma (HCC). Materials and Methods In this retrospective, multicenter study, 578 patients with HCC (mean age [±SD], 59 years ± 10; 442 male, 136 female) were divided into the training (n = 317), internal (n = 137), and external (n = 124) test sets. DL radiomics and pathomics models were developed to predict VETC using gadoxetic acid-enhanced MR and pathologic images. Deep radiomics score (DRS) and handcrafted and deep pathomics scores were compared between the group with VETC pattern in HCC (VETC+) and group without VETC pattern in HCC (VETC-). Multivariable Cox regression analyses were performed to identify independent prognostic factors, and the radiopathomics nomogram models were developed for early recurrence and progression-free survival (PFS). The prognostic power was evaluated using the concordance index (C index) and time-dependent receiver operating characteristic (ROC) curves. Results In the external test set, the Swin Transformer showed good performance for predicting VETC in both DL radiomics (area under the ROC curve [AUC], 0.77-0.79) and pathomics (AUC, 0.79) models. Patients with VETC+ HCC had significantly higher DRS and handcrafted and deep pathomics scores compared with patients with VETC- HCC in all datasets (all P < .001). The radiopathomics nomogram model incorporating DRS in the arterial phase and the handcrafted and deep pathomics scores achieved C indexes of 0.69, 0.60, and 0.67 for early recurrence and time-dependent AUCs of 0.83 (95% CI: 0.76, 0.91), 0.81 (95% CI: 0.68, 0.94), and 0.78 (95% CI: 0.67, 0.88) for 3-year PFS in the training, internal, and external test sets, respectively. Early recurrence and PFS rates statistically significantly differed between the high- and low-risk patients stratified by the radiopathomics nomogram model (all P < .05). Conclusion DL radiopathomics models effectively helped to predict VETC in HCC and assess the risk for early recurrence and PFS. Keywords: Hepatocellular Carcinoma, Deep Learning, MRI, Radiopathomics, Survival Supplemental material is available for this article. © RSNA, 2025.

PMID:40084948 | DOI:10.1148/rycan.240213

J Chem Inf Model. 2025 Mar 14. doi: 10.1021/acs.jcim.5c00135. Online ahead of print.

ABSTRACT

Ischemic stroke's complex pathophysiology demands therapeutic approaches targeting multiple pathways simultaneously, yet current treatments remain limited. We developed an innovative drug discovery pipeline combining a deep learning approach with experimental validation to identify natural compounds with comprehensive neuroprotective properties. Our computational framework integrated SELFormer, a transformer-based deep learning model, and multiple deep learning algorithms to predict NC bioactivity against seven crucial stroke-related targets (ACE, GLA, MMP9, NPFFR2, PDE4D, and eNOS). The pipeline encompassed IC50 predictions, clustering analysis, quantitative structure-activity relationship (QSAR) modeling, and uniform manifold approximation and projection (UMAP)-based bioactivity profiling followed by molecular docking studies and experimental validation. Analysis revealed six distinct NC clusters with unique molecular signatures. UMAP projection identified 11 medium-activity (6 < pIC50 ≤ 7) and 57 high-activity (pIC50 > 7) compounds, with molecular docking confirming strong correlations between binding energies and predicted pIC50 values. In vitro studies using NGF-differentiated PC12 cells under oxygen-glucose deprivation demonstrated significant neuroprotective effects of four high-activity compounds: feruloyl glucose, l-hydroxy-l-tryptophan, mulberrin, and ellagic acid. These compounds enhanced cell viability, reduced acetylcholinesterase activity and lipid peroxidation, suppressed TNF-α expression, and upregulated BDNF mRNA levels. Notably, mulberrin and ellagic acid showed superior efficacy in modulating oxidative stress, inflammation, and neurotrophic signaling. This study establishes a robust deep learning-driven framework for identifying multitarget natural therapeutics for ischemic stroke. The validated compounds, particularly mulberrin and ellagic acid, are promising for stroke treatment development. Our findings demonstrate the effectiveness of integrating computational prediction with experimental validation in accelerating drug discovery for complex neurological disorders.

PMID:40084909 | DOI:10.1021/acs.jcim.5c00135

Recenti Prog Med. 2025 Mar;116(3):156-161. doi: 10.1701/4460.44555.

ABSTRACT

Pain is a complex, multidimensional experience involving significant challenges in both diagnosis and management. While acute pain serves as a critical warning mechanism, chronic pain encompasses intricate biological, psychological, and social components, complicating its assessment and treatment. Artificial intelligence (AI) technologies are revolutionizing medicine and healthcare. Here we present an overview of the recent advances in AI for pain medicine. For example, the emergence of automatic pain assessment (APA) methodologies offers promising avenues for more objective pain evaluation. For APA aims, AI technologies, including machine learning algorithms and deep learning architectures such as natural language processing systems, have shown potential in analyzing biosignals, facial expressions, and speech patterns related to pain. However, the integration of these objective measures with traditional self-reporting remains essential for a comprehensive approach to pain diagnosis. Notably, APA models can be implemented for pain diagnosis in newborn and non-communicative patients. Additionally, the application of AI extends beyond pain diagnosis to personalized treatment strategies, predict opioid use disorders, education and training, clinical trajectory definition, and telehealth and real-time. Despite the potential of these innovations, challenges such as validation, parameter selection, and ethical aspects of technical implementation must be addressed.

PMID:40084580 | DOI:10.1701/4460.44555

J Biophotonics. 2025 Mar 14:e70006. doi: 10.1002/jbio.70006. Online ahead of print.

ABSTRACT

Melasma is a common pigmentary disorder accompanied by tissue changes in composition and structure through the epidermis and dermis. In this study, we propose to employ optical coherence tomography (OCT) combined with deep learning techniques for melasma diagnostics. Specifically, a portable spectral domain OCT system with a handheld probe was developed for clinical skin imaging. Then, a diagnostic model was built based on the VGG16 neural network by adding a spatial attention mechanism. The results show that a good differentiation with an accuracy of 94.2% can be achieved among health datasets from healthy volunteers, and melasma and tissue-around-melasma datasets from melasma patients. Moreover, the same trained model was applied to treatment evaluation, showing a good capability to assess antivascular medicine treatment. Thus, it can be concluded that OCT combined with deep learning techniques has a good potential to aid in clinical diagnosis and treatment evaluation of melasma.

PMID:40084480 | DOI:10.1002/jbio.70006

Am J Cancer Res. 2025 Feb 15;15(2):754-768. doi: 10.62347/XKFN1793. eCollection 2025.

ABSTRACT

Breast cancer is a disorder affecting women globally, and hence an early and precise classification is the best possible treatment to increase the survival rate. However, the breast cancer classification faced difficulties in scalability, fixed-size input images, and overfitting on limited datasets. To tackle these issues, this work proposes a Patho-Net model for breast cancer classification that overcomes the problems of scalability in color normalization, integrates the Gated Recurrent Unit (GRU) network with the U-Net architecture to process images without the need for resizing and computational efficiency, and addresses the overfitting problems. The proposed model collects and normalizes histopathology images using automated reference image selection with the Reinhard method for color standardization. Also, the Enhanced Adaptive Non-Local Means (EANLM) filtering is utilized for noise removal to preserve image features. These preprocessed images undergo semantic segmentation to isolate specific parts of an image, followed by feature extraction using an Improved Gray Level Co-occurrence Matrix (I-GLCM) to reveal fine patterns and textures in images. These features serve as input into the classification U-Net model integrated with GRU networks to improve the model performance. Finally, the classification result is expanded, and XAI is used for clear visual explanations of the model's predictions. The proposed Patho-Net model, which uses the 100X BreakHis dataset, achieves an accuracy of 98.90% in the classification of breast cancer.

PMID:40084355 | PMC:PMC11897615 | DOI:10.62347/XKFN1793

Int J Med Sci. 2025 Feb 18;22(6):1375-1392. doi: 10.7150/ijms.107694. eCollection 2025.

ABSTRACT

Background: Pyrimidine metabolism is a hallmark of tumor metabolic reprogramming, while its significance in the prognostic and therapeutic implications of patients with lung adenocarcinoma (LUAD) still remains unclear. Methods: In this study, an integrated framework of various machine learning and deep learning algorithms was used to develop the pyrimidine metabolism-related signature (PMRS). Its efficacy in genomic stability, chemotherapy and immunotherapy resistance was evaluated through comprehensive multi-omics analysis. The single-cell landscape of patients between PMRS subgroups was also elucidated. Subsequently, the biological functions of LYPD3, the most important coefficient factor in the PMRS model, were experimentally validated in LUAD cell lines. Results: The PMRS model with "random survival forest" algorithm exhibited the best performance and was utilized for further analysis. It displayed excellent accuracy and stability in various model evaluation assays. Compared to the PMRS-high subgroup, patients with lower PMRS scores had better survival outcomes, more stable genomic characteristics and higher sensitivity to immunotherapy. Single-cell analysis indicated that as PMRS increased, epithelial cells gradually exhibited malignant phenotypes with enhanced pyrimidine metabolism, while PMRS-high patients showed an inhibitory status of tumor immune microenvironment. Further experiments indicated that LYPD3 promoted the malignant progression in LUAD cell lines. Conclusion: Our study constructed the PMRS model, highlighting its potential value in the treatment and prognosis of LUAD patients and providing new insights into the individualized precision treatment for LUAD patients.

PMID:40084259 | PMC:PMC11898844 | DOI:10.7150/ijms.107694

BME Front. 2025 Mar 13;6:0110. doi: 10.34133/bmef.0110. eCollection 2025.

ABSTRACT

Objective: We developed 3-dimensional spatially resolved gene neighborhood network embedding (3D-spaGNN-E) to find subcellular gene proximity relationships and identify key subcellular motifs in cell-cell communication (CCC). Impact Statement: The pipeline combines 3D imaging-based spatial transcriptomics and graph-based deep learning to identify subcellular motifs. Introduction: Advancements in imaging and experimental technology allow the study of 3D spatially resolved transcriptomics and capture better spatial context than approximating the samples as 2D. However, the third spatial dimension increases the data complexity and requires new analyses. Methods: 3D-spaGNN-E detects single transcripts in 3D cell culture samples and identifies subcellular gene proximity relationships. Then, a graph autoencoder projects the gene proximity relationships into a latent space. We then applied explainability analysis to identify subcellular CCC motifs. Results: We first applied the pipeline to mesenchymal stem cells (MSCs) cultured in hydrogel. After clustering the cells based on the RNA count, we identified cells belonging to the same cluster as homotypic and those belonging to different clusters as heterotypic. We identified changes in local gene proximity near the border between homotypic and heterotypic cells. When applying the pipeline to the MSC-peripheral blood mononuclear cell (PBMC) coculture system, we identified CD4+ and CD8+ T cells. Local gene proximity and autoencoder embedding changes can distinguish strong and weak suppression of different immune cells. Lastly, we compared astrocyte-neuron CCC in mouse hypothalamus and cortex by analyzing 3D multiplexed-error-robust fluorescence in situ hybridization (MERFISH) data and identified regional gene proximity differences. Conclusion: 3D-spaGNN-E distinguished distinct CCCs in cell culture and tissue by examining subcellular motifs.

PMID:40084126 | PMC:PMC11906096 | DOI:10.34133/bmef.0110

Heliyon. 2025 Jan 18;11(4):e42111. doi: 10.1016/j.heliyon.2025.e42111. eCollection 2025 Feb 28.

ABSTRACT

We present a novel method for the label-free detection of amyloid-beta (Aβ) plaques, the key hallmark of Alzheimer's disease, in human brain tissue sections. Conventionally, immunohistochemistry (IHC) is employed for the characterization of Aβ plaques, hindering subsequent analysis. Here, a semi-supervised convolutional neural network (CNN) is trained to detect Aβ plaques in quantum cascade laser infrared (QCL-IR) microscopy images. Laser microdissection (LMD) is then used to precisely extract plaques from snap-frozen, unstained tissue sections. Mass spectrometry-based proteomics reveals a loss of soluble proteins in IHC stained samples. Our method prevents this loss and provides a novel tool that expands the scope of molecular analysis methods to chemically native plaques. Insight into soluble plaque components will complement our understanding of plaques and their role in Alzheimer's disease.

PMID:40083995 | PMC:PMC11903818 | DOI:10.1016/j.heliyon.2025.e42111

Front Neuroinform. 2025 Feb 27;19:1521805. doi: 10.3389/fninf.2025.1521805. eCollection 2025.

ABSTRACT

Analysis of electroencephalography (EEG) signals gathered by brain-computer interface (BCI) recently demonstrated that deep neural networks (DNNs) can be effectively used for investigation of time sequences for physical actions (PA) classification. In this study, the relatively simple DNN with fully connected network (FCN) components and convolutional neural network (CNN) components was considered to classify finger-palm-hand manipulations each from the grasp-and-lift (GAL) dataset. The main aim of this study was to imitate and investigate environmental influence by the proposed noise data augmentation (NDA) of two kinds: (i) natural NDA by inclusion of noise EEG data from neighboring regions by increasing the sampling size N and the different offset values for sample labeling and (ii) synthetic NDA by adding the generated Gaussian noise. The natural NDA by increasing N leads to the higher micro and macro area under the curve (AUC) for receiver operating curve values for the bigger N values than usage of synthetic NDA. The detrended fluctuation analysis (DFA) was applied to investigate the fluctuation properties and calculate the correspondent Hurst exponents H for the quantitative characterization of the fluctuation variability. H values for the low time window scales (< 2 s) are higher in comparison with ones for the bigger time window scales. For example, H more than 2-3 times higher for some PAs, i.e., it means that the shorter EEG fragments (< 2 s) demonstrate the scaling behavior of the higher complexity than the longer fragments. As far as these results were obtained by the relatively small DNN with the low resource requirements, this approach can be promising for porting such models to Edge Computing infrastructures on devices with the very limited computational resources.

PMID:40083893 | PMC:PMC11903462 | DOI:10.3389/fninf.2025.1521805

Am J Hosp Palliat Care. 2025 Mar 14:10499091251325566. doi: 10.1177/10499091251325566. Online ahead of print.

ABSTRACT

BackgroundDyspnea is a prevalent and distressing symptom in interstitial lung diseases with significant effects on patients' quality of life and associated with poorer prognosis. Guidelines recommend a multidimensional dyspnea assessment tool. We developed a validated 9-item scale, the Edmonton Dyspnea Inventory (EDI), in which dyspnea severity is rated across different settings including at rest, during activities of daily living, and self-reported exercise and crises. The standardized, multidimensional tool captures dyspnea intensity for specific contexts, which clinicians can use to manage dyspnea more individually and effectively. Early studies support the feasibility to use the EDI in outpatient settings. The purpose of this study was to explore perceptions of the EDI by community health care professionals.MethodsWe conducted a qualitative study using an inductive approach and open coding for content analysis. Email invitations were sent to community health care professionals and informed consent obtained from the twelve participants. Two focus groups and one key informant interview were conducted. Themes were extracted from transcripts and field note analyses.ResultsFour main themes described their dyspnea assessment with the EDI: the EDI is a meaningful clinical assessment tool; they explicitly engage and educate patients to effectively use the EDI; they use the EDI to personalize and evaluate dyspnea management; and the EDI is valuable for communication and interprofessional collaboration.ConclusionCommunity health care professionals perceived the EDI as valuable to assess dyspnea and personalize management. They recommended it be used in clinical practice and healthcare education for interprofessional dyspnea management for ILD patients.

PMID:40085021 | DOI:10.1177/10499091251325566

J Rehabil Med Clin Commun. 2025 Mar 6;8:40698. doi: 10.2340/jrm-cc.v8.40698. eCollection 2025.

ABSTRACT

OBJECTIVE: To evaluate the benefits of inpatient rehabilitation for a patient with post-COVID-19 pulmonary fibrosis and to provide guidance for rehabilitation professionals, as many conventional therapeutic interventions are not tolerated and are poorly defined.

DESIGN: A case report.

SUBJECTS/PATIENTS: A 72-year-old man with a COVID-19-related idiopathic pulmonary fibrosis exacerbation.

RESULTS: The patient was admitted to inpatient rehabilitation with hypoxia and poor endurance for functional activities. Rehabilitation activities were focused on providing patient/family education, energy conservation, low level activities to build strength, problem solving for mobility, and discharge planning within safe medical parameters. Rehabilitation therapies were graded to meet the patient's physiologic needs and focused on patient and family training. The patient made limited functional gains and continued to have high oxygen needs but achieved his goal of returning home.

CONCLUSION: Patients with COVID-19-related idiopathic pulmonary fibrosis exacerbations can be treated in acute rehabilitation effectively. With more patients developing post-COVID-19 pulmonary fibrosis, appropriate rehabilitation strategies are important for safe discharge planning. Prioritizing patient/family education may allow these more medically fragile patients to return home.

PMID:40083891 | PMC:PMC11905151 | DOI:10.2340/jrm-cc.v8.40698

Ther Adv Respir Dis. 2025 Jan-Dec;19:17534666251326743. doi: 10.1177/17534666251326743. Epub 2025 Mar 14.

ABSTRACT

BACKGROUND: Pulmonary fibrosis is a severe, progressive form of interstitial lung disease associated with increased morbidity and mortality. Pulmonary hypertension often accompanies severe pulmonary fibrosis and is also associated with worse outcomes. Antifibrotic therapy and pulmonary vasodilator therapy have demonstrated clinical benefits in pulmonary fibrosis and pulmonary hypertension, respectively. However, the benefit of combined antifibrotic and pulmonary vasodilator therapy in patients with both pulmonary fibrosis and pulmonary hypertension is less established.

OBJECTIVES: We aimed to determine the effectiveness of a combination pulmonary vasodilator and antifibrotic therapy with regard to transplant-free survival and six-minute walk distance improvement in patients with pulmonary fibrosis and pulmonary hypertension.

DESIGN: This was a retrospective cohort study of patients with pulmonary fibrosis (idiopathic pulmonary fibrosis, combined pulmonary fibrosis and emphysema, and other fibrotic interstitial lung disease) and pulmonary hypertension diagnosed via right heart catheterization. Patients received antifibrotic therapy with or without pulmonary vasodilator therapy.

METHODS: Patients who received combination antifibrotic therapy and pulmonary vasodilator therapy were compared to those prescribed antifibrotic therapy alone. Transplant-free survival and change in six-minute walk distance were compared between the two groups. Multivariable Cox regression was performed to determine predictors of transplant-free survival.

RESULTS: Patients who received antifibrotic and pulmonary vasodilator therapy had significantly improved transplant-free survival (log rank p = 0.001). Treatment with antifibrotic and pulmonary vasodilator therapy was significantly and independently associated with reduced risk of death or lung transplantation (HR 0.24, 95% CI 0.06-0.93, p = 0.04). These patients had worse pulmonary hemodynamics than those receiving antifibrotic therapy alone.

CONCLUSION: We found a potential survival benefit when pulmonary vasodilator therapy was given in combination with antifibrotic therapy in patients with pulmonary fibrosis and pulmonary hypertension. This may be reflective of a pulmonary vascular phenotype among those with pulmonary fibrosis and pulmonary hypertension. Further trials are needed to better elucidate which patients benefit from combination therapy.

PMID:40083194 | DOI:10.1177/17534666251326743

Cancer Immunol Res. 2025 Mar 14:OF1-OF20. doi: 10.1158/2326-6066.CIR-24-0151. Online ahead of print.

ABSTRACT

Immunotherapies have had unprecedented success in the treatment of multiple cancer types, albeit with variable response rates. Unraveling the complex network of immune cells within the tumor microenvironment (TME) may provide additional insights to enhance antitumor immunity and improve clinical response. Many studies have shown that NK cells or innate lymphoid cells (ILC) have regulatory capacity. Here, we identified CD103 as a marker that was found on CD56+ cells that were associated with a poor proliferative capacity of tumor-infiltrating lymphocytes in culture. We further demonstrated that CD103+CD56+ ILCs isolated directly from tumors represented a distinct ILC population that expressed unique surface markers (such as CD49a and CD101), transcription factor networks, and transcriptomic profiles compared with CD103-CD56+ NK cells. Using single-cell multiomic and spatial approaches, we found that these CD103+CD56+ ILCs were associated with CD8+ T cells with reduced expression of granzyme B. Thus, this study identifies a population of CD103+CD56+ ILCs with potentially inhibitory functions that are associated with a TME that includes CD8+ T cells with poor antitumor activity. Further studies focusing on these cells may provide additional insights into the biology of an inhibitory TME.

PMID:40084939 | DOI:10.1158/2326-6066.CIR-24-0151

J Proteome Res. 2025 Mar 14. doi: 10.1021/acs.jproteome.4c01020. Online ahead of print.

ABSTRACT

We describe a new release of the Candida albicans PeptideAtlas proteomics spectral resource (build 2024-03), providing a sequence coverage of 79.5% at the canonical protein level, matched mass spectrometry spectra, and experimental evidence identifying 3382 and 536 phosphorylated serine and threonine sites with false localization rates of 1% and 5.3%, respectively. We provide a tutorial on how to use the PeptideAtlas and associated tools to access this information. The C. albicans PeptideAtlas summary web page provides "Build overview", "PTM coverage", "Experiment contribution", and "Data set contribution" information. The protein and peptide information can also be accessed via the Candida Genome Database via hyperlinks on each protein page. This allows users to peruse identified peptides, protein coverage, post-translational modifications (PTMs), and experiments that identify each protein. Given the value of understanding the PTM landscape in the sequence of each protein, a more detailed explanation of how to interpret and analyze PTM results is provided in the PeptideAtlas of this important pathogen. Candida albicans PeptideAtlas web page: https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/buildDetails?atlas_build_id=578.

PMID:40084908 | DOI:10.1021/acs.jproteome.4c01020

Bio Protoc. 2025 Mar 5;15(5):e5223. doi: 10.21769/BioProtoc.5223. eCollection 2025 Mar 5.

ABSTRACT

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. According to 2020 reports, globally, 2.2 million cases are reported every year, with the mortality number being as high as 1.8 million patients. To study NSCLC, systems biology offers mathematical modeling as a tool to understand complex pathways and provide insights into the identification of biomarkers and potential therapeutic targets, which aids precision therapy. Mathematical modeling, specifically ordinary differential equations (ODEs), is used to better understand the dynamics of cancer growth and immunological interactions in the tumor microenvironment. This study highlighted the dual role of the cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS/STING) pathway's classical involvement in regulating type 1 interferon (IFN I) and pro-inflammatory responses to promote tumor regression through senescence and apoptosis. Alternative signaling was induced by nuclear factor kappa B (NF-κB), mutated tumor protein p53 (p53), and programmed death-ligand1 (PD-L1), which lead to tumor growth. We identified key regulators in cancer progression by simulating the model and validating it with the following model estimation parameters: local sensitivity analysis, principal component analysis, rate of flow of metabolites, and model reduction. Integration of multiple signaling axes revealed that cGAS-STING, phosphoinositide 3-kinases (PI3K), and Ak strain transforming (AKT) may be potential targets that can be validated for cancer therapy. Key features • Procedures for the reconstruction of a robust and steady-state mathematical model with respective analysis in order to provide mechanistic insights. • The dynamic mathematical model allows an understanding of the multifaceted dual roles of cGAS-STING in NSCLC promotion and inhibition. • The inherent statistical tool in systems biology provides a novel immunotherapeutic target.

PMID:40084069 | PMC:PMC11896782 | DOI:10.21769/BioProtoc.5223

Front Immunol. 2025 Feb 27;16:1530053. doi: 10.3389/fimmu.2025.1530053. eCollection 2025.

ABSTRACT

OBJECTIVE: To observe the inhibitory effect and potential mechanism of Yiqi Huayu Jiedu Decoction (YHJD) on liver metastasis of colorectal cancer (CRC).

METHODS: We compared the changes of liver weight and liver index before and after YHJD treatment in CRC liver metastasis mouse models. HE staining was employed to observe the pathological changes in mouse liver tissue sections. Flow cytometry was used to analyze the number and marker of neutrophils treated with YHJD. Transcriptomics, proteomics, and multiplex cytokine array analyses were conducted to further verify the role of YHJD on CXCL1. Differential gene analysis was performed to further explore the mechanism by which YHJD inhibits liver metastasis of CRC.

RESULTS: Animal studies demonstrated that YHJD reduces liver metastases. Flow cytometry results revealed that YHJD promotes N1 neutrophils in liver. Combining multi-omics and multiple cytokine arrays, we observed a significant increase in the expression of CXCL1 in the liver and plasma. GO and KEGG enrichment analyses indicated that YHJD may regulate the chemotaxis of neutrophils to inhibit the liver metastasis of CRC by participating in the regulation of cell adhesion molecule binding, adhesion protein binding, and multiple metabolic pathways.

CONCLUSIONS: YHJD inhibits CRC liver metastasis by upregulating CXCL1, thereby promoting N1 neutrophil chemotaxis towards the liver, and concurrently raising the expression of N1 neutrophil markers.

PMID:40083557 | PMC:PMC11903724 | DOI:10.3389/fimmu.2025.1530053

J Public Health Afr. 2025 Feb 25;16(1):690. doi: 10.4102/jphia.v16i1.690. eCollection 2025.

ABSTRACT

BACKGROUND: Human immunodeficiency viruses (HIV) and acquired immunodeficiency syndrome (AIDS) remain a global public health problem. Other sexually transmitted infections (STIs) are aggravating factors.

AIM: This study aimed to assess the prevalence and identify new cases of HIV and STIs, as well as their associated risk factors.

SETTING: Political insecurity in the northern regions of Cameroon has led to population displacement, weakening an already fragile health system.

METHODS: A cohort of 684 consenting participants from the north and far north were enrolled in 2021 and followed up in 2022. Socio-demographic variables and risk behaviours were collected. Anti-HIV Ab, hepatitis B surface antigen, Treponema pallidum haemagglutination tests were performed. The data were analysed using Epi Info 7.5.2. The associations between variables were evaluated using the Chi-square test with a 95% confidence interval.

RESULTS: The new cases of HIV rate and overall prevalence were 1.63% (95% confidence interval [CI]: 0.83% - 2.41%) and 3.8% (95% CI: 2.01% - 3.97%), respectively. New HIV cases increased from 0.27% (2017, Demographic and Health Survey [DHS]) to 1.63%. The prevalence of syphilis and hepatitis B was 1.03% (95% CI: 0.98% - 1.09%) and 4.56% (95% CI: 4.51% - 4.66%), respectively. Factors associated with HIV included religion (p = 0.027), unprotected sex (p = 0.006), sex with a sex worker (p = 0.00009), and co-infection with syphilis and hepatitis B (p = 0.033). New HIV infections may also be associated with population displacement.

CONCLUSION: HIV infection, syphilis and hepatitis B are on the rise in the Northern Cameroon.

CONTRIBUTION: Future HIV prevention strategies should consider population displacement and HIV-associated infections such as hepatitis B and syphilis in order to secure achievements in HIV programme and further curb the burden of these infections in the country.

PMID:40083464 | PMC:PMC11905195 | DOI:10.4102/jphia.v16i1.690

Aging Cell. 2025 Mar 13:e70043. doi: 10.1111/acel.70043. Online ahead of print.

ABSTRACT

The lymphatic vasculature plays essential roles in fluid balance, immunity, and lipid transport. Chronic, low-grade inflammation in peripheral tissues develops when lymphatic structure or function is impaired, as observed during aging. While aging has been associated with a broad range of heart pathophysiology, its effect on cardiac lymphatic vasculature has not been characterized. Here, we analyzed cardiac lymphatics in aged 20-month-old mice versus young 2-month-old mice. Aged hearts showed reduced lymphatic vascular density, more dilated vessels, and increased inflammation and fibrosis in peri-lymphatic zones. As exercise has shown benefits in several different models of age-related heart disease, we further investigated the effects of aerobic training on cardiac lymphatics. Eight weeks of voluntary wheel running attenuated age-associated adverse remodeling of the cardiac lymphatics, including reversing their dilation, increasing lymph vessel density and branching, and reducing perilymphatic inflammation and fibrosis. Intravital lymphangiography demonstrated improved cardiac lymphatic flow after exercise training. Our findings illustrate that aging leads to cardiac lymphatic dysfunction, and that exercise can improve lymphatic health in aged animals.

PMID:40083143 | DOI:10.1111/acel.70043

FEMS Yeast Res. 2025 Mar 13:foaf011. doi: 10.1093/femsyr/foaf011. Online ahead of print.

ABSTRACT

The formation of hyphae is one of the most crucial virulence traits the human pathogenic fungus Candida albicans possesses. The assessment of hyphal length in response to various stimuli, such as exposure to human serum, provides valuable insights into the adaptation strategies of C. albicans to the host environment. Despite the increasing high-throughput capacity live-cell imaging and data generation, the accurate analysis of hyphal growth has remained a laborious, error-prone, and subjective manual process. We developed an analysis pipeline utilizing the open-source visual programming language JIPipe to overcome the limitations associated with manual analysis of hyphal growth. By comparing our automated approach with manual analysis, we refined the strategies to achieve accurate differentiation between yeast cells and hyphae. The automated method enables length measurements of individual hyphae, facilitating a time-efficient, high-throughput, and user-friendly analysis. By utilizing this JIPipe analysis approach, we obtained insights into the filamentation behavior of two C. albicans strains when exposed to human serum albumin (HSA), the most abundant protein in human serum. Our findings indicate that despite the known role of HSA in stimulating fungal growth, it reduces filamentous growth. The implementation of our automated JIPipe analysis approach for hyphal growth represents a long-awaited and time-efficient solution to meet the demand of high-throughput data generation. This tool can benefit different research areas investigating the virulence aspects of C. albicans.

PMID:40082735 | DOI:10.1093/femsyr/foaf011

EMBO J. 2025 Mar 13. doi: 10.1038/s44318-025-00408-1. Online ahead of print.

ABSTRACT

The AAA protease FtsH associates with HflK/C subunits to form a megadalton-size complex that spans the inner membrane and extends into the periplasm of E. coli. How this bacterial complex and homologous assemblies in eukaryotic organelles recruit, extract, and degrade membrane-embedded substrates is unclear. Following the overproduction of protein components, recent cryo-EM structures showed symmetric HflK/C cages surrounding FtsH in a manner proposed to inhibit the degradation of membrane-embedded substrates. Here, we present structures of native protein complexes, in which HflK/C instead forms an asymmetric nautilus-shaped assembly with an entryway for membrane-embedded substrates to reach and be engaged by FtsH. Consistent with this nautilus-like structure, proteomic assays suggest that HflK/C enhances FtsH degradation of certain membrane-embedded substrates. Membrane curvature in our FtsH•HflK/C complexes is opposite that of surrounding membrane regions, a property that correlates with lipid scramblase activity and possibly with FtsH's function in the degradation of membrane-embedded proteins.

PMID:40082723 | DOI:10.1038/s44318-025-00408-1