PLoS One. 2025 Feb 21;20(2):e0316989. doi: 10.1371/journal.pone.0316989. eCollection 2025.

ABSTRACT

Hyperpartisan news consists of articles with strong biases that support specific political parties. The spread of such news increases polarization among readers, which threatens social unity and democratic stability. Automated tools can help identify hyperpartisan news in the daily flood of articles, offering a way to tackle these problems. With recent advances in machine learning and deep learning, there are now more methods available to address this issue. This literature review collects and organizes the different methods used in previous studies on hyperpartisan news detection. Using the PRISMA methodology, we reviewed and systematized approaches and datasets from 81 articles published from January 2015 to 2024. Our analysis includes several steps: differentiating hyperpartisan news detection from similar tasks, identifying text sources, labeling methods, and evaluating models. We found some key gaps: there is no clear definition of hyperpartisanship in Computer Science, and most datasets are in English, highlighting the need for more datasets in minority languages. Moreover, the tendency is that deep learning models perform better than traditional machine learning, but Large Language Models' (LLMs) capacities in this domain have been limitedly studied. This paper is the first to systematically review hyperpartisan news detection, laying a solid groundwork for future research.

PMID:39982955 | DOI:10.1371/journal.pone.0316989

PLoS One. 2025 Feb 21;20(2):e0313947. doi: 10.1371/journal.pone.0313947. eCollection 2025.

ABSTRACT

Bacteriophages largely impact bacterial communities via lysis, gene transfer, and metabolic reprogramming and thus are increasingly thought to alter nutrient and energy cycling across many of Earth's ecosystems. However, there are few model systems to mechanistically and quantitatively study phage-bacteria interactions, especially in soil systems. Here, we isolated, sequenced, and genomically characterized 24 novel phages infecting Klebsiella sp. M5al, a plant growth-promoting, nonencapsulated rhizosphere-associated bacterium, and compared many of their features against all 565 sequenced, dsDNA Klebsiella phage genomes. Taxonomic analyses revealed that these Klebsiella phages belong to three known phage families (Autographiviridae, Drexlerviridae, and Straboviridae) and two newly proposed phage families (Candidatus Mavericviridae and Ca. Rivulusviridae). At the phage family level, we found that core genes were often phage-centric proteins, such as structural proteins for the phage head and tail and DNA packaging proteins. In contrast, genes involved in transcription, translation, or hypothetical proteins were commonly not shared or flexible genes. Ecologically, we assessed the phages' ubiquity in recent large-scale metagenomic datasets, which revealed they were not widespread, as well as a possible direct role in reprogramming specific metabolisms during infection by screening their genomes for phage-encoded auxiliary metabolic genes (AMGs). Even though AMGs are common in the environmental literature, only one of our phage families, Straboviridae, contained AMGs, and the types of AMGs were correlated at the genus level. Host range phenotyping revealed the phages had a wide range of infectivity, infecting between 1-14 of our 22 bacterial strain panel that included pathogenic Klebsiella and Raoultella strains. This indicates that not all capsule-independent Klebsiella phages have broad host ranges. Together, these isolates, with corresponding genome, AMG, and host range analyses, help build the Klebsiella model system for studying phage-host interactions of rhizosphere-associated bacteria.

PMID:39982899 | DOI:10.1371/journal.pone.0313947

JMIR Nurs. 2025 Feb 21;8:e60810. doi: 10.2196/60810.

ABSTRACT

BACKGROUND: The digitalization of health care in Germany holds great potential to improve patient care, resource management, and efficiency. However, strict data protection regulations, fragmented infrastructures, and resistance to change hinder progress. These challenges leave care institutions reliant on outdated paper-based workflows, particularly for patient data transmission, despite the pressing need for efficient tools to support health care professionals amid a nursing shortage and rising demand for care.

OBJECTIVE: This paper aims to analyze Germany's care transition record (CTR) and CTR transmission process as part of transition management and suggests improvements toward a seamless digital solution.

METHODS: To understand the current challenges of manual CTR transfers, we used a mixed methods approach, which included a web-based questionnaire with nursing professionals, field observations, business process model and notation modeling, semantic and frequency analysis of CTR entries, and user story mapping.

RESULTS: A web-based questionnaire involving German nursing professionals (N=59) revealed considerable delays in patient care due to manual, patient-transferred CTRs. Of the 33 usable responses (n=33), 70% (n=23) of the respondents advocating for digital transmission to improve efficiency. Observations (N=11) in care facilities (n=5, 45%) and a hospital (n=6, 55%) confirmed the high administrative burden, averaging 34.67 (SD 10.78) minutes per CTR within a hospital and 44.6 (SD 20.5) minutes in care facilities. A semantic analysis of various CTRs (N=4) highlighted their differences and complexity, stressing the need for standardization. Analyzing a new CTR standard (care information object CTR) and manually mapping an existing CTR to it showed that the procedure was ambiguous, and some associations remained unclear. A frequency analysis of CTR entities revealed which were most used. In addition, discussions with care staff pointed out candidates for the most relevant entities. On the basis of the key findings, a stepwise transition approach toward a road map proposal for a standardized, secure transfer of CTRs was conceptualized. This road map in the form of a user story map, encompassing a "CTR transformer" (mapping of traditional CTRs to a new standard) and "care information object CTR viewer/editor" (in short, CIO-CTR viewer and editor; a new standard for viewing, editing, and exporting), shows a possibility to bridge the transition time until all institutions fully support the new standard.

CONCLUSIONS: A future solution should simplify the overall CTR transmission process by minimizing manual transfers into in-house systems, standardizing the CTR, and providing a secure digital transfer. This could positively impact the overall care process and patient experience. With our solutions, we attempt to support care staff in their daily activities and processes until nationwide state regulations are implemented successfully, though the timeline for this remains uncertain.

PMID:39982779 | DOI:10.2196/60810

Int J Neonatal Screen. 2025 Jan 27;11(1):10. doi: 10.3390/ijns11010010.

ABSTRACT

The Portuguese Newborn Screening Program currently includes 28 pathologies: congenital hypothyroidism, cystic fibrosis, 24 inborn errors of metabolism, sickle cell disease and spinal muscular atrophy. This pilot study for sickle cell disease newborn screening, including 188,217 samples, was performed between May 2021 and December 2023, with phase I, including 24,130 newborns, in the Lisbon and Setubal districts and phase II, including 164,087 newborns, in the whole country. DBS samples were analyzed through capillary electrophoresis. In phase I, a high birth incidence of sickle cell disease was found (1:928 NBs), resulting from the identification of 24 HbSS and 2 HbSC patients. This birth incidence decreased but remained significant when the pilot study for sickle cell disease newborn screening was expanded to a national level, with the identification of 67 sickle cell disease patients (59 HbSS and 8 HbSC), revealing a birth incidence of 1:2449 NBs. These data suggest that this condition is becoming increasingly relevant in Portugal, thus reflecting a general European trend, where sickle cell disease is already recognized as a public health problem. Therefore, it highlights the importance of its integration into the Portuguese National Newborn Screening Program panel in January 2024, thus allowing the early identification and clinical follow-up of these patients.

PMID:39982344 | DOI:10.3390/ijns11010010

Antibodies (Basel). 2025 Feb 14;14(1):18. doi: 10.3390/antib14010018.

ABSTRACT

Immunoglobulin Y (IgY) is the primary antibody found in the eggs of chicken (Gallus domesticus), allowing for large-scale antibody production with high titers, making them cost-effective antibody producers. IgY serves as a valuable alternative to mammalian antibodies typically used in immunodiagnostics and immunotherapy. Compared to mammalian antibodies, IgY offers several biochemical advantages, and its straightforward purification from egg yolk eliminates the need for invasive procedures like blood collection, reducing stress in animals. Due to the evolutionary differences between birds and mammals, chicken antibodies can bind to a broader range of epitopes on mammalian proteins than their mammalian counterparts. Studies have shown that chicken antibodies bind 3-5 times more effectively to rabbit IgG than swine antibodies, enhancing the signal in immunological assays. Additionally, IgY does not interact with rheumatoid factors or human anti-mouse IgG antibodies (HAMA), helping to minimize interference from these factors. IgY obtained from egg yolk of hens immunized against Pseudomonas aeruginosa has been used in patients suffering from cystic fibrosis and chronic pulmonary colonization with this bacterium. Furthermore, IgY has been used to counteract streptococcus mutans in the oral cavity and for the treatment of enteral infections in both humans and animals. However, the use of avian antibodies is limited to pulmonary, enteral, or topical application and should, due to immunogenicity, not be used for systemic administration. Thus, IgY expands the range of strategies available for combating pathogens in medicine, as a promising candidate both as an alternative to antibiotics and as a valuable tool in research and diagnostics.

PMID:39982233 | DOI:10.3390/antib14010018

Immunol Rev. 2025 Mar;330(1):e70009. doi: 10.1111/imr.70009.

ABSTRACT

Cystic fibrosis (CF) is a common autosomal recessive disease resulting from mutations of the gene that encodes the cystic fibrosis transmembrane conductance regulator (CFTR). Although severe pulmonary neutrophilic inflammation is a primary pathologic feature of CF, more recent studies reveal a role for type 2 inflammation that is characterized by eosinophilia directed by both the innate and adaptive immune systems through ILC2 and CD4+ Th2 cells, respectively. We have published that a clear type endotype exists within CF subjects stratified by Th2 inflammation, defined by increased obstructive pulmonary disease and a distinct phenotypic signature of increased allergic disease, infections, and burden of CF complications. Further, we showed an increased risk of death among CF subjects with type 2 inflammatory signatures compared to CF subjects lacking significant type 2 inflammation. The mechanisms of this heightened type 2 inflammatory signature in CF are still being defined, but it is clear that airway epithelial cells from CFTR-deficient mice have increased expression and release of IL-33, a key activator of ILC2 and Th2 cells, compared to persons with normal CFTR function. Further, there is strong evidence that CF regulates CD4+ Th2 function in a cell-intrinsic fashion. These concepts are explored in this review article.

PMID:39981881 | DOI:10.1111/imr.70009

Ophthalmol Ther. 2025 Feb 21. doi: 10.1007/s40123-025-01109-y. Online ahead of print.

ABSTRACT

Fundus fluorescein angiography (FFA) serves as the current gold standard for visualizing retinal vasculature and detecting various fundus diseases, but its interpretation is labor-intensive and requires much expertise from ophthalmologists. The medical application of artificial intelligence (AI), especially deep learning and machine learning, has revolutionized the field of automatic FFA image analysis, leading to the rapid advancements in AI-assisted lesion detection, diagnosis, and report generation. This review examined studies in PubMed, Web of Science, and Google Scholar databases from January 2019 to August 2024, with a total of 23 articles incorporated. By integrating current research findings, this review highlights crucial breakthroughs in AI-assisted FFA analysis and explores their potential implications for ophthalmic clinical practice. These advances in AI-assisted FFA analysis have shown promising results in improving diagnostic accuracy and workflow efficiency. However, further research is needed to enhance model transparency and ensure robust performance across diverse populations. Challenges such as data privacy and technical infrastructure remain for broader clinical applications.

PMID:39982648 | DOI:10.1007/s40123-025-01109-y

Ann Hematol. 2025 Feb 21. doi: 10.1007/s00277-025-06254-9. Online ahead of print.

ABSTRACT

Monitoring anemia recovery is crucial for clinical intervention. Morphological assessment of red blood cells (RBCs) with peripheral blood smears (PBSs) provides additional information beyond routine blood tests. However, the PBS test is labor-intensive, reliant on manual analysis, and susceptible to variability in expert interpretations. Here we introduce a deep semi-supervised learning method, RBCMatch, to classify RBCs during anemia recovery. Using an acute hemolytic anemic mouse model, PBS images at four different time points during anemia recovery were acquired and segmented into 10,091 single RBC images, with only 5% annotated and used in model training. By employing the semi-supervised strategy Fixmatch, RBCMatch achieved an impressive average classification accuracy of 91.2% on the validation dataset and 87.5% on a held-out dataset, demonstrating its superior accuracy and robustness compared to supervised learning methods, especially when labeled samples are scarce. To characterize the anemia recovery process, principal components (PCs) of RBC embeddings were extracted and visualized. Our results indicated that RBC embeddings quantified the state of anemia recovery, and the second PC had a strong correlation with RBC count and hemoglobin concentration, demonstrating the model's ability to accurately depict RBC morphological changes during anemia recovery. Thus, this study provides a valuable tool for the automatic classification of RBCs and offers novel insights into the assessment of anemia recovery, with the potential to aid in clinical decision-making and prognosis analysis in the future.

PMID:39982510 | DOI:10.1007/s00277-025-06254-9

Radiologie (Heidelb). 2025 Feb 21. doi: 10.1007/s00117-025-01416-2. Online ahead of print.

ABSTRACT

BACKGROUND: Given the increasing number of radiological examinations, large language models (LLMs) offer promising support in radiology. Optimized interaction is essential to ensure reliable results.

OBJECTIVES: This article provides an overview of interaction techniques such as prompt engineering, zero-shot learning, and retrieval-augmented generation (RAG) and gives practical tips for their application in radiology.

MATERIALS AND METHODS: Demonstration of interaction techniques based on practical examples with concrete recommendations for their application in routine radiological practice.

RESULTS: Advanced interaction techniques allow task-specific adaptation of LLMs without the need for retraining. The creation of precise prompts and the use of zero-shot and few-shot learning can significantly improve response quality. RAG enables the integration of current and domain-specific information into LLM tools, increasing the accuracy and relevance of the generated content.

CONCLUSIONS: The use of prompt engineering, zero-shot and few-shot learning, and RAG can optimize interaction with LLMs in radiology. Through these targeted strategies, radiologists can efficiently integrate general chatbots into routine practice to improve patient care.

PMID:39982460 | DOI:10.1007/s00117-025-01416-2

Proteomes. 2025 Feb 17;13(1):10. doi: 10.3390/proteomes13010010.

ABSTRACT

Background: The interaction network between the human host proteins and the proteins of the gut bacteria is essential for the establishment of human health, and its dysregulation directly contributes to disease development. Despite its great importance, experimental data on protein-protein interactions (PPIs) between these species are sparse due to experimental limitations. Methods: This study presents a deep learning-based framework for predicting PPIs between human and gut bacterial proteins using structural data. The framework leverages graph-based protein representations and variational autoencoders (VAEs) to extract structural embeddings from protein graphs, which are then fused through a Bi-directional Cross-Attention module to predict interactions. The model addresses common challenges in PPI datasets, such as class imbalance, using focal loss to emphasize harder-to-classify samples. Results: The results demonstrated that this framework exhibits robust performance, with high precision and recall across validation and test datasets, underscoring its generalizability. By incorporating proteoforms in the analysis, the model accounts for the structural complexity within proteomes, making predictions biologically relevant. Conclusions: These findings offer a scalable tool for investigating the interactions between the host and the gut microbiota, potentially yielding new treatment targets and diagnostics for disorders linked to the microbiome.

PMID:39982320 | DOI:10.3390/proteomes13010010

Radiol Imaging Cancer. 2025 Mar;7(2):e240161. doi: 10.1148/rycan.240161.

ABSTRACT

Purpose To develop a radiomic graph framework, RadGraph, for spatial analysis of pretreatment CT images to improve prediction of local-regional recurrence (LR) and distant metastasis (DM) in head and neck squamous cell carcinoma (HNSCC). Materials and Methods This retrospective study included four public pre-radiotherapy treatment CT datasets of patients with HNSCC obtained from The Cancer Imaging Archive (images collected between 2003 and 2018). Computational graphs and graph attention deep learning methods were leveraged to holistically model multiple regions in the head and neck anatomy. Clinical features, including age, sex, and human papillomavirus infection status, were collected for a baseline model. Model performance in predicting LR and DM was evaluated via area under the receiver operating characteristic curve (AUC) and qualitative interpretation of model attention. Results A total of 3434 patients (61 years ± 11 [SD], 2774 male) were divided into training (n = 1576), validation (n = 379), and testing (n = 1479) datasets. RadGraph achieved AUCs of up to 0.83 and 0.90 for LR and DM prediction, respectively. RadGraph showed higher performance compared with the clinical baseline (AUCs up to 0.73 for LR prediction and 0.83 for DM prediction) and previously published approaches (AUCs up to 0.81 for LR prediction and 0.87 for DM prediction). Graph attention atlases enabled visualization of regions coinciding with cervical lymph node chains as important for outcome prediction. Conclusion RadGraph leveraged information from tumor and nontumor regions to effectively predict LR and DM in a large multi-institutional dataset of patients with radiation therapy-treated HNSCC. Graph attention atlases enabled interpretation of model predictions. Keywords: CT, Informatics, Neural Networks, Radiation Therapy, Head/Neck, Computer Applications-General (Informatics), Tumor Response, Head and Neck Squamous Cell Carcinoma, Locoregional Recurrence, Radiotherapy, Deep Learning, Radiomics Supplemental material is available for this article. © RSNA, 2025.

PMID:39982207 | DOI:10.1148/rycan.240161

Cytometry A. 2025 Feb 21. doi: 10.1002/cyto.a.24917. Online ahead of print.

ABSTRACT

Alterations in nuclear morphology are useful adjuncts and even diagnostic tools used by pathologists in the diagnosis and grading of many tumors, particularly malignant tumors. Large datasets such as TCGA and the Human Protein Atlas, in combination with emerging machine learning and statistical modeling methods, such as feature extraction and deep learning techniques, can be used to extract meaningful knowledge from images of nuclei, particularly from cancerous tumors. Here, we describe a new technique based on the mathematics of optimal transport for modeling the information content related to nuclear chromatin structure directly from imaging data. In contrast to other techniques, our method represents the entire information content of each nucleus relative to a template nucleus using a transport-based morphometry (TBM) framework. We demonstrate that the model is robust to different staining patterns and imaging protocols, and can be used to discover meaningful and interpretable information within and across datasets and cancer types. In particular, we demonstrate morphological differences capable of distinguishing nuclear features along the spectrum from benign to malignant categories of tumors across different cancer tissue types, including tumors derived from liver parenchyma, thyroid gland, lung mesothelium, and skin epithelium. We believe these proof-of-concept calculations demonstrate that the TBM framework can provide the quantitative measurements necessary for performing meaningful comparisons across a wide range of datasets and cancer types that can potentially enable numerous cancer studies, technologies, and clinical applications and help elevate the role of nuclear morphometry into a more quantitative science.

PMID:39982036 | DOI:10.1002/cyto.a.24917

J Comput Chem. 2025 Mar 5;46(6):e70060. doi: 10.1002/jcc.70060.

ABSTRACT

Scoring functions (SFs) of molecular docking is a vital component of structure-based virtual screening (SBVS). Traditional SFs yield their inherent shortage for idealized approximations and simplifications predicting the binding affinity. Complementarily, SFs based on deep learning (DL) have emerged as powerful tools for capturing intricate features within protein-ligand (PL) interactions. We here present a docking-score fusion strategy that integrates pose scores derived from GNINA's convolutional neural network (CNN) with traditional docking scores. Extensive validation on diverse datasets has shown that by means of multiplying Watvina docking score by CNNscore demonstrates state-of-the-art screening power. Furthermore, in a reverse practice, our docking-score fusion technique was incorporated into the virtual screening (VS) workflow aimed at identifying inhibitors of the challenging target TYK2. Two promising hits with IC50 9.99 μM and 13.76 μM in vitro were identified from nearly 12 billion molecules.

PMID:39981784 | DOI:10.1002/jcc.70060

Mol Biol Rep. 2025 Feb 21;52(1):258. doi: 10.1007/s11033-025-10330-8.

ABSTRACT

BACKGROUND: Host DNA depletion is a critical tool for accessing the microbiomes of samples that have a small amount of microbial DNA contained in a high host background. Of critical practical importance is the ability to identify microbial DNA sequences in frozen tissue specimens. Here, we compare four existing commercial methods and two newly introduced methods involving chromatin immunoprecipitation (ChIP) on frozen human and pig intestinal biopsies.

RESULTS: We find that all methods that rely on differential lysis of host and microbial cells introduce substantial biases as assessed by 16 S rRNA gene amplicon profiling. However, ChIP enables 10-fold enrichment of microbial DNA while introducing less bias, sufficient to make assessment possible against background, in both pigs and humans.

CONCLUSIONS: We recommend ChIP in situations where host depletion is important but where minimizing taxonomic bias is essential, and the MolYsis or Zymo kit for situations where host depletion level is more important than taxonomic bias.

CONCLUSIONS: We recommend ChIP in situations where host depletion is important but where minimizing taxonomic bias is essential, and the MolYsis or Zymo kit for situations where host depletion level is more important than taxonomic bias.

PMID:39982577 | DOI:10.1007/s11033-025-10330-8

Ann Hematol. 2025 Feb 21. doi: 10.1007/s00277-025-06254-9. Online ahead of print.

ABSTRACT

Monitoring anemia recovery is crucial for clinical intervention. Morphological assessment of red blood cells (RBCs) with peripheral blood smears (PBSs) provides additional information beyond routine blood tests. However, the PBS test is labor-intensive, reliant on manual analysis, and susceptible to variability in expert interpretations. Here we introduce a deep semi-supervised learning method, RBCMatch, to classify RBCs during anemia recovery. Using an acute hemolytic anemic mouse model, PBS images at four different time points during anemia recovery were acquired and segmented into 10,091 single RBC images, with only 5% annotated and used in model training. By employing the semi-supervised strategy Fixmatch, RBCMatch achieved an impressive average classification accuracy of 91.2% on the validation dataset and 87.5% on a held-out dataset, demonstrating its superior accuracy and robustness compared to supervised learning methods, especially when labeled samples are scarce. To characterize the anemia recovery process, principal components (PCs) of RBC embeddings were extracted and visualized. Our results indicated that RBC embeddings quantified the state of anemia recovery, and the second PC had a strong correlation with RBC count and hemoglobin concentration, demonstrating the model's ability to accurately depict RBC morphological changes during anemia recovery. Thus, this study provides a valuable tool for the automatic classification of RBCs and offers novel insights into the assessment of anemia recovery, with the potential to aid in clinical decision-making and prognosis analysis in the future.

PMID:39982510 | DOI:10.1007/s00277-025-06254-9

Proteomes. 2025 Feb 10;13(1):9. doi: 10.3390/proteomes13010009.

ABSTRACT

Background: Chinese hamster ovary (CHO) cells are extensively used in the pharmaceutical industry for producing complex proteins, primarily because of their ability to perform human-like post-translational modifications. However, the efficiency of high-quality protein production can vary significantly for monoclonal antibody-producing cell lines, within the CHO host cell lines or by extrinsic factors. Methods: To investigate the complex cellular mechanisms underlying this variability, a phosphoproteomics analysis was performed using label-free quantitative liquid chromatography after a phosphopeptide enrichment of recombinant CHO cells producing two different antibodies and a tunicamycin treatment experiment. Using MaxQuant and Perseus for data analysis, we identified 2109 proteins and quantified 4059 phosphosites. Results: Significant phosphorylation dynamics were observed in nuclear proteins of cells producing the difficult-to-produce 2G12 mAb. It suggests that the expression of 2G12 regulates nuclear pathways based on increases and decreases in phosphorylation abundance. Furthermore, a substantial number of changes in the phosphorylation pattern related to tunicamycin treatment have been detected. TM treatment affects, among other phosphoproteins, the eukaryotic elongation factor 2 kinase (Eef2k). Conclusions: The alterations in the phosphorylation landscape of key proteins involved in cellular processes highlight the mechanisms behind stress-induced cellular responses.

PMID:39982319 | DOI:10.3390/proteomes13010009

J Funct Morphol Kinesiol. 2025 Feb 10;10(1):61. doi: 10.3390/jfmk10010061.

ABSTRACT

Background/Objectives: In recent years, there has been a significant increase in studies examining motor learning during preschool age and the early years of primary school. This study aimed to investigate sex differences in gross motor competence among Italian children aged 3-11 years. Methods: A convenience sample of 8500 children (mean age = 8.37 years, SD = 1.98; 50% female) was included in this cross-sectional study. Gross motor skills were assessed using the Italian version of the Test of Gross Motor Development-3, which evaluates locomotion and ball control skills. A Linear Mixed Model was applied to examine the interaction between sex and age, with school included as a random intercept and BMI as a covariate. Results: The results revealed a consistent trend of boys achieving significantly higher total scores for global motor competence (p < 0.001) across all age groups, except at age 11. Boys also demonstrated superior performance in ball control skills (p < 0.005) at all ages. In contrast, no significant differences were observed for locomotion skills overall. However, girls outperform boys in locomotor skills at ages 6, 7, and 8 (p < 0.001), with this trend disappearing by age 9. Conclusions: These findings highlight important sex-related differences in gross motor development during childhood, influenced by both biological and environmental factors. The results underscore the need for targeted interventions in educational settings to provide equitable opportunities for motor skill development, particularly for girls. Enhancing the quality of physical education and addressing gender disparities can support the acquisition of essential motor skills and promote lifelong physical activity.

PMID:39982301 | DOI:10.3390/jfmk10010061

Epigenomes. 2025 Feb 5;9(1):5. doi: 10.3390/epigenomes9010005.

ABSTRACT

Genomic and epigenomic instability are defining features of cancer, driving tumor progression, heterogeneity, and therapeutic resistance. Central to this process are epigenetic echoes, persistent and dynamic modifications in DNA methylation, histone modifications, non-coding RNA regulation, and chromatin remodeling that mirror underlying genomic chaos and actively influence cancer cell behavior. This review delves into the complex relationship between genomic instability and these epigenetic echoes, illustrating how they collectively shape the cancer genome, affect DNA repair mechanisms, and contribute to tumor evolution. However, the dynamic, context-dependent nature of epigenetic changes presents scientific and ethical challenges, particularly concerning privacy and clinical applicability. Focusing on lung cancer, we examine how specific epigenetic patterns function as biomarkers for distinguishing cancer subtypes and monitoring disease progression and relapse.

PMID:39982247 | DOI:10.3390/epigenomes9010005

Cytometry A. 2025 Feb 21. doi: 10.1002/cyto.a.24917. Online ahead of print.

ABSTRACT

Alterations in nuclear morphology are useful adjuncts and even diagnostic tools used by pathologists in the diagnosis and grading of many tumors, particularly malignant tumors. Large datasets such as TCGA and the Human Protein Atlas, in combination with emerging machine learning and statistical modeling methods, such as feature extraction and deep learning techniques, can be used to extract meaningful knowledge from images of nuclei, particularly from cancerous tumors. Here, we describe a new technique based on the mathematics of optimal transport for modeling the information content related to nuclear chromatin structure directly from imaging data. In contrast to other techniques, our method represents the entire information content of each nucleus relative to a template nucleus using a transport-based morphometry (TBM) framework. We demonstrate that the model is robust to different staining patterns and imaging protocols, and can be used to discover meaningful and interpretable information within and across datasets and cancer types. In particular, we demonstrate morphological differences capable of distinguishing nuclear features along the spectrum from benign to malignant categories of tumors across different cancer tissue types, including tumors derived from liver parenchyma, thyroid gland, lung mesothelium, and skin epithelium. We believe these proof-of-concept calculations demonstrate that the TBM framework can provide the quantitative measurements necessary for performing meaningful comparisons across a wide range of datasets and cancer types that can potentially enable numerous cancer studies, technologies, and clinical applications and help elevate the role of nuclear morphometry into a more quantitative science.

PMID:39982036 | DOI:10.1002/cyto.a.24917

Protein Sci. 2025 Mar;34(3):e70069. doi: 10.1002/pro.70069.

ABSTRACT

Chikungunya virus (CHIKV) is an emerging pathogen with pandemic potential. CHIKV infection in humans is transmitted by mosquitoes and induces common symptoms of high fever, arthralgia and myalgia. Because no specific antiviral drugs for treatment of CHIKV infection are available, drug development remains a central goal. The chikungunya virus protease from nsP2 (CHIKVP) has emerged as a key drug target due to its indispensable role in viral replication via cleavage of the viral polyprotein. To date, effective tools for screening for CHIKVP inhibitors that reflect the most critical polyprotein cleavage sites have been lacking, hampering drug-development efforts. We found that the recognition ability of CHIKVP is sensitive to the length of peptide substrates. In this study, we report a robust fluorogenic substrate comprising a 15-mer peptide derived from the nsP3/4 junction from the CHIKV polyprotein. This peptide is flanked by an ACC-Lys(dnp) donor-quencher pair. Our new substrate acc-CHIK15-dnp shows a 30-fold improved signal-to-noise ratio as compared to the previously reported edab8 substrate, which is also based on the nsP3/4 junction. We found acc-CHIK15-dnp is recognized only by CHIKVP but not by other alphavirus proteases. This is surprising due to the high level of sequence conservation in the alpha virus polyprotein junctions and indicates that the P-side residues are more important than the P'-side sequence for effective CHIKVP cleavage. The robust signal-to-noise ratio obtained using acc-CHIK15-dnp derived from the nsP3/4 cleavage site enabled much improved small molecule HTS on CHIKV relative to other fluorogenic reporters.

PMID:39981948 | DOI:10.1002/pro.70069