J Chem Theory Comput. 2025 Mar 13. doi: 10.1021/acs.jctc.4c01774. Online ahead of print.

ABSTRACT

Protein evolution has shaped enzymes that maintain stability and function across diverse thermal environments. While sequence variation, thermal stability and conformational dynamics are known to influence an enzyme's thermal adaptation, how these factors collectively govern stability and function across diverse temperatures remains unresolved. Cytosolic malate dehydrogenase (cMDH), a citric acid cycle enzyme, is an ideal model for studying these mechanisms due to its temperature-sensitive flexibility and broad presence in species from diverse thermal environments. In this study, we employ techniques inspired by deep learning and statistical mechanics to uncover how sequence variation and conformational dynamics shape patterns of cMDH's thermal adaptation. By integrating coevolutionary models with variational autoencoders (VAE), we generate a latent generative landscape (LGL) of the cMDH sequence space, enabling us to explore mutational pathways and predict fitness using direct coupling analysis (DCA). Structure predictions via AlphaFold and molecular dynamics simulations further illuminate how variations in hydrophobic interactions and conformational flexibility contribute to the thermal stability of warm- and cold-adapted cMDH orthologs. Notably, we identify the ratio of hydrophobic contacts between two regions as a predictive order parameter for thermal stability features, providing a quantitative metric for understanding cMDH dynamics across temperatures. The integrative computational framework employed in this study provides mechanistic insights into protein adaptation at both sequence and structural levels, offering unique perspectives on the evolution of thermal stability and creating avenues for the rational design of proteins with optimized thermal properties.

PMID:40079215 | DOI:10.1021/acs.jctc.4c01774

Nanoscale. 2025 Mar 13. doi: 10.1039/d4nr03860f. Online ahead of print.

ABSTRACT

In order to relate nanoparticle properties to function, fast and detailed particle characterization is needed. The ability to characterize nanoparticle samples using optical microscopy techniques has drastically improved over the past few decades; consequently, there are now numerous microscopy methods available for detailed characterization of particles with nanometric size. However, there is currently no "one size fits all" solution to the problem of nanoparticle characterization. Instead, since the available techniques have different detection limits and deliver related but different quantitative information, the measurement and analysis approaches need to be selected and adapted for the sample at hand. In this tutorial, we review the optical theory of single particle scattering and how it relates to the differences and similarities in the quantitative particle information obtained from commonly used label-free microscopy techniques, with an emphasis on nanometric (submicron) sized dielectric particles. Particular emphasis is placed on how the optical signal relates to mass, size, structure, and material properties of the detected particles and to its combination with diffusivity-based particle sizing. We also discuss emerging opportunities in the wake of new technology development, including examples of adaptable python notebooks for deep learning image analysis, with the ambition to guide the choice of measurement strategy based on various challenges related to different types of nanoparticle samples and associated analytical demands.

PMID:40079204 | DOI:10.1039/d4nr03860f

Network. 2025 Mar 13:1-32. doi: 10.1080/0954898X.2025.2461046. Online ahead of print.

ABSTRACT

Non-Orthogonal Multiple Access (NOMA) is the successive multiple-access methodologies for modern communication devices. Energy Efficiency (EE) is suggested in the NOMA system. In dynamic network conditions, the consideration of NOMA shows high computational complexity that minimizes the EE to degrade the system performance. This research suggested EE for the Multi-Carrier NOMA (MC-NOMA) models by optimization algorithm. The main scope of this research tends to improve the EE by Hybrid of Sewing Training and Lemur Optimization for optimizing the system parameters. The improvement made in this developed HSTLO algorithm can provide significant impact on MC-NOMA system, which it renders better user capacity while effectively optimizing the system parameters. Moreover, the Dilated Dense Recurrent Neural Network (DDRNN) model is developed. Employing the improvement in the deep learning model for the MC-NOMA system could effectively manage and enhance the system performance. Considering the DDRNN model can leverage to provide better generalization outcomes in different network scenarios that ensures to provide fast and reliable solutions compared to existing methods. Addressing the energy consumption problems in this research study will be analysed to show the advancement in MC-NOMA system that help to enhance the system performance.

PMID:40079096 | DOI:10.1080/0954898X.2025.2461046

J Med Imaging (Bellingham). 2025 Nov;12(Suppl 2):S22002. doi: 10.1117/1.JMI.12.S2.S22002. Epub 2025 Mar 11.

ABSTRACT

PURPOSE: Breast density (BD) and background parenchymal enhancement (BPE) are important imaging biomarkers for breast cancer (BC) risk. We aim to evaluate longitudinal changes in quantitative BD and BPE in high-risk women undergoing dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), focusing on the effects of age and transition into menopause.

APPROACH: A retrospective cohort study analyzed 834 high-risk women undergoing breast DCE-MRI for screening between 2005 and 2020. Quantitative BD and BPE were derived using deep-learning segmentation. Linear mixed-effects models assessed longitudinal changes and the effects of age, menopausal status, weeks since the last menstrual period (LMP-wks), body mass index (BMI), and hormone replacement therapy (HRT) on these imaging biomarkers.

RESULTS: BD decreased with age across all menopausal stages, whereas BPE declined with age in postmenopausal women but remained stable in premenopausal women. HRT elevated BPE in postmenopausal women. Perimenopausal women exhibited decreases in both BD and BPE during the menopausal transition, though cross-sectional age at menopause had no significant effect on either measure. Fibroglandular tissue was positively associated with BPE in perimenopausal women.

CONCLUSIONS: We highlight the dynamic impact of menopause on BD and BPE and correlate well with the known relationship between risk and age at menopause. These findings advance the understanding of imaging biomarkers in high-risk populations and may contribute to the development of improved risk assessment leading to personalized chemoprevention and BC screening recommendations.

PMID:40078986 | PMC:PMC11894108 | DOI:10.1117/1.JMI.12.S2.S22002

R Soc Open Sci. 2025 Mar 12;12(3):241222. doi: 10.1098/rsos.241222. eCollection 2025 Mar.

ABSTRACT

Medical image classification plays an important role in medical imaging. In this work, we present a novel approach to enhance deep learning models in medical image classification by incorporating clinical variables without overwhelming the information. Unlike most existing deep neural network models that only consider single-pixel information, our method captures a more comprehensive view. Our method contains two main steps and is effective in tackling the extra challenge raised by the scarcity of medical data. Firstly, we employ a pre-trained deep neural network served as a feature extractor to capture meaningful image features. Then, an exquisite discriminant analysis is applied to reduce the dimensionality of these features, ensuring that the low number of features remains optimized for the classification task and striking a balance with the clinical variables information. We also develop a way of obtaining class activation maps for our approach in visualizing models' focus on specific regions within the low-dimensional feature space. Thorough experimental results demonstrate improvements of our proposed method over state-of-the-art methods for tuberculosis and dermatology issues for example. Furthermore, a comprehensive comparison with a popular dimensionality reduction technique (principal component analysis) is also conducted.

PMID:40078919 | PMC:PMC11897822 | DOI:10.1098/rsos.241222

EURASIP J Audio Speech Music Process. 2025;2025(1):12. doi: 10.1186/s13636-025-00400-x. Epub 2025 Mar 10.

ABSTRACT

This paper introduces singing to speech conversion (S2S), a cross-domain voice conversion task, and presents the first deep learning-based S2S system. S2S aims to transform singing into speech while retaining the phonetic information, reducing variations in pitch, rhythm, and timbre. Inspired by the Glow-TTS architecture, the proposed model is built using generative flow, with an adjusted alignment module between the latent features. We adapt the original monotonic alignment search (MAS) to the S2S scenario and utilize a duration predictor to deal with the duration differences between the two modalities. Subjective evaluations show that the proposed model outperforms signal processing baselines in naturalness and outperforms a transcribe-and-synthesize baseline in phonetic similarity to the original singing. We further demonstrate that singing-to-speech could be an effective augmentation method for low-resource lyrics transcription.

PMID:40078713 | PMC:PMC11893632 | DOI:10.1186/s13636-025-00400-x

Front Surg. 2025 Feb 26;12:1528362. doi: 10.3389/fsurg.2025.1528362. eCollection 2025.

ABSTRACT

OBJECTIVE: This systematic literature review of the integration of artificial intelligence (AI) applications in surgical practice through hand and instrument tracking provides an overview of recent advancements and analyzes current literature on the intersection of surgery with AI. Distinct AI algorithms and specific applications in surgical practice are also examined.

METHODS: An advanced search using medical subject heading terms was conducted in Medline (via PubMed), SCOPUS, and Embase databases for articles published in English. A strict selection process was performed, adhering to PRISMA guidelines.

RESULTS: A total of 225 articles were retrieved. After screening, 77 met inclusion criteria and were included in the review. Use of AI algorithms in surgical practice was uncommon during 2013-2017 but has gained significant popularity since 2018. Deep learning algorithms (n = 62) are increasingly preferred over traditional machine learning algorithms (n = 15). These technologies are used in surgical fields such as general surgery (n = 19), neurosurgery (n = 10), and ophthalmology (n = 9). The most common functional sensors and systems used were prerecorded videos (n = 29), cameras (n = 21), and image datasets (n = 7). The most common applications included laparoscopic (n = 13), robotic-assisted (n = 13), basic (n = 12), and endoscopic (n = 8) surgical skills training, as well as surgical simulation training (n = 8).

CONCLUSION: AI technologies can be tailored to address distinct needs in surgical education and patient care. The use of AI in hand and instrument tracking improves surgical outcomes by optimizing surgical skills training. It is essential to acknowledge the current technical and social limitations of AI and work toward filling those gaps in future studies.

PMID:40078701 | PMC:PMC11897506 | DOI:10.3389/fsurg.2025.1528362

Chin J Cancer Res. 2025 Jan 30;37(1):28-47. doi: 10.21147/j.issn.1000-9604.2025.01.03.

ABSTRACT

OBJECTIVE: Early predicting response before neoadjuvant chemotherapy (NAC) is crucial for personalized treatment plans for locally advanced breast cancer patients. We aim to develop a multi-task model using multiscale whole slide images (WSIs) features to predict the response to breast cancer NAC more finely.

METHODS: This work collected 1,670 whole slide images for training and validation sets, internal testing sets, external testing sets, and prospective testing sets of the weakly-supervised deep learning-based multi-task model (DLMM) in predicting treatment response and pCR to NAC. Our approach models two-by-two feature interactions across scales by employing concatenate fusion of single-scale feature representations, and controls the expressiveness of each representation via a gating-based attention mechanism.

RESULTS: In the retrospective analysis, DLMM exhibited excellent predictive performance for the prediction of treatment response, with area under the receiver operating characteristic curves (AUCs) of 0.869 [95% confidence interval (95% CI): 0.806-0.933] in the internal testing set and 0.841 (95% CI: 0.814-0.867) in the external testing sets. For the pCR prediction task, DLMM reached AUCs of 0.865 (95% CI: 0.763-0.964) in the internal testing and 0.821 (95% CI: 0.763-0.878) in the pooled external testing set. In the prospective testing study, DLMM also demonstrated favorable predictive performance, with AUCs of 0.829 (95% CI: 0.754-0.903) and 0.821 (95% CI: 0.692-0.949) in treatment response and pCR prediction, respectively. DLMM significantly outperformed the baseline models in all testing sets (P<0.05). Heatmaps were employed to interpret the decision-making basis of the model. Furthermore, it was discovered that high DLMM scores were associated with immune-related pathways and cells in the microenvironment during biological basis exploration.

CONCLUSIONS: The DLMM represents a valuable tool that aids clinicians in selecting personalized treatment strategies for breast cancer patients.

PMID:40078559 | PMC:PMC11893347 | DOI:10.21147/j.issn.1000-9604.2025.01.03

Chin J Cancer Res. 2025 Jan 30;37(1):12-27. doi: 10.21147/j.issn.1000-9604.2025.01.02.

ABSTRACT

OBJECTIVE: The neglect of occult lymph nodes metastasis (OLNM) is one of the pivotal causes of early non-small cell lung cancer (NSCLC) recurrence after local treatments such as stereotactic body radiotherapy (SBRT) or surgery. This study aimed to develop and validate a computed tomography (CT)-based radiomics and deep learning (DL) fusion model for predicting non-invasive OLNM.

METHODS: Patients with radiologically node-negative lung adenocarcinoma from two centers were retrospectively analyzed. We developed clinical, radiomics, and radiomics-clinical models using logistic regression. A DL model was established using a three-dimensional squeeze-and-excitation residual network-34 (3D SE-ResNet34) and a fusion model was created by integrating seleted clinical, radiomics features and DL features. Model performance was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis (DCA). Five predictive models were compared; SHapley Additive exPlanations (SHAP) and Gradient-weighted Class Activation Mapping (Grad-CAM) were employed for visualization and interpretation.

RESULTS: Overall, 358 patients were included: 186 in the training cohort, 48 in the internal validation cohort, and 124 in the external testing cohort. The DL fusion model incorporating 3D SE-Resnet34 achieved the highest AUC of 0.947 in the training dataset, with strong performance in internal and external cohorts (AUCs of 0.903 and 0.907, respectively), outperforming single-modal DL models, clinical models, radiomics models, and radiomics-clinical combined models (DeLong test: P<0.05). DCA confirmed its clinical utility, and calibration curves demonstrated excellent agreement between predicted and observed OLNM probabilities. Features interpretation highlighted the importance of textural characteristics and the surrounding tumor regions in stratifying OLNM risk.

CONCLUSIONS: The DL fusion model reliably and accurately predicts OLNM in early-stage lung adenocarcinoma, offering a non-invasive tool to refine staging and guide personalized treatment decisions. These results may aid clinicians in optimizing surgical and radiotherapy strategies.

PMID:40078558 | PMC:PMC11893343 | DOI:10.21147/j.issn.1000-9604.2025.01.02

Front Hum Neurosci. 2025 Feb 26;19:1517273. doi: 10.3389/fnhum.2025.1517273. eCollection 2025.

ABSTRACT

With the rapid development of deep learning, Electroencephalograph(EEG) emotion recognition has played a significant role in affective brain-computer interfaces. Many advanced emotion recognition models have achieved excellent results. However, current research is mostly conducted in laboratory settings for emotion induction, which lacks sufficient ecological validity and differs significantly from real-world scenarios. Moreover, emotion recognition models are typically trained and tested on datasets collected in laboratory environments, with little validation of their effectiveness in real-world situations. VR, providing a highly immersive and realistic experience, is an ideal tool for emotional research. In this paper, we collect EEG data from participants while they watched VR videos. We propose a purely Transformer-based method, EmoSTT. We use two separate Transformer modules to comprehensively model the temporal and spatial information of EEG signals. We validate the effectiveness of EmoSTT on a passive paradigm collected in a laboratory environment and an active paradigm emotion dataset collected in a VR environment. Compared with state-of-the-art methods, our method achieves robust emotion classification performance and can be well transferred between different emotion elicitation paradigms.

PMID:40078487 | PMC:PMC11897567 | DOI:10.3389/fnhum.2025.1517273

Int J Mol Sci. 2025 Mar 5;26(5):2327. doi: 10.3390/ijms26052327.

ABSTRACT

Filtration bleb (FB) fibrosis represents the primary risk factor for glaucoma filtration surgery (GFS) failure. We reviewed the most recent literature on post-GFS fibrosis in humans, focusing on novel molecular pathways and antifibrotic treatments. Three main literature searches were conducted. First, we performed a narrative review of two models of extra-ocular fibrosis, idiopathic pulmonary fibrosis and skin fibrosis, to improve the comprehension of ocular fibrosis. Second, we conducted a systematic review of failed FB features in the PubMed, Embase, and Cochrane Library databases. Selected studies were screened based on the functional state and morphological features of FB. Third, we carried out a narrative review of novel potential antifibrotic molecules. In the systematic review, 11 studies met the criteria for analysis. Immunohistochemistry and genomics deemed SPARC and transglutaminases to be important for tissue remodeling and attributed pivotal roles to TGFβ and M2c macrophages in promoting FB fibrosis. Four major mechanisms were identified in the FB failure process: inflammation, fibroblast proliferation and myofibroblast conversion, vascularization, and tissue remodeling. On this basis, an updated model of FB fibrosis was described. Among the pharmacological options, particular attention was given to nintedanib, pirfenidone, and rapamycin, which are used in skin and pulmonary fibrosis, since their promising effects are demonstrated in experimental models of FB fibrosis. Based on the most recent literature, modern patho-physiological models of FB fibrosis should consider TGFβ and M2c macrophages as pivotal players and favorite targets for therapy, while research on antifibrotic strategies should clinically investigate medications utilized in the management of extra-ocular fibrosis.

PMID:40076946 | DOI:10.3390/ijms26052327

Int J Mol Sci. 2025 Feb 28;26(5):2218. doi: 10.3390/ijms26052218.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a rare disorder concerning elderly people, predominantly men, active or former smokers, with a progressive nature and leading to premature mortality. The cause of the disease is unknown. However, there are some risk factors, among which genetic predisposition plays a role. The aim of our single-centered observational study was to assess the correlation between single nucleotide polymorphism (SNP) of the MUC5B gene (rs35705950) and the disease course, antifibrotic treatment effect, and survival in patients with IPF. A total of 93 patients entered the study, of whom 88 were treated with either nintedanib or pirfenidone. The GG genotype was found in 28 (30.1%) subjects, while the GT or TT genotypes were found in the remaining 65 (63.4%) and 6 (6.5%) patients, respectively. The T allele minor allele frequency (MAF) accounted for 38.2% of the whole group. Patients with different genotypes did not differ significantly regarding age, sex, pulmonary function tests' results, response to the antifibrotic treatment, or survival. However, we found a survival advantage in female patients and patients with higher pre-treatment TL,co. Treatment with antifibrotics significantly decreased the magnitude of FVC and TL,co decline compared to the time before treatment initiation, regardless of MUC5B status. In conclusion, we found high prevalence of T allele of MUC5B gene in patients with IPF; however, it showed no influence on disease trajectory, survival, or antifibrotic treatment effect in the presented cohort.

PMID:40076835 | DOI:10.3390/ijms26052218

Sci Rep. 2025 Mar 13;15(1):8622. doi: 10.1038/s41598-025-93217-9.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a kind of interstitial lung disease (ILD). It has a high incidence rate and mortality. Its pathogenesis remains unclear. So far, no effective methods have been found for the early diagnosis of IPF. Ferroptosis has been reported to be critical in the initiation and progression of IPF. Therefore, our aim was to identify the hub gene related to ferroptosis co-expressed in the peripheral blood and pulmonary tissue of patients with IPF. Sequencing data were obtained from the Gene Expression Omnibus database. A comprehensive analysis was conducted on the differentially expressed genes (DEGs) to extract ferroptosis-related differentially expressed genes (FRDEGs). The results showed that ferroptosis-related signal paths were highly enriched in IPF, and 10 FRDEGs were identified.The hub gene was predicted through protein-protein interactions (PPI) and Cytoscape. The diagnostic utility of the hub gene was proven by enzyme-linked immunosorbent assay (ELISA) in serum and by immunohistochemistry (IHC) in pulmonary tissues. The results of ELISA indicated that the levels of ATM in the serum of patients with IPF were significantly lower than the normal levels. In contrast, the results of IHC showed that the expression of ATM in the pulmonary tissues of IPF patients exhibited a notably elevated trend. The immune status was assessed by the CIBERSORT method and so was the relevance between ATM and immune cells. These findings unveiled significant differences in various immune cell types in peripheral blood and pulmonary tissue between the IPF group and the control group. Furthermore, ATM was associated with various immune cells. This study suggests that as a ferroptosis-related gene, ATM assumes a pivotal role in the diagnosis and treatment of IPF. This discovery presents a novel approach for the clinical diagnosis and therapy of IPF.

PMID:40075162 | DOI:10.1038/s41598-025-93217-9

Lung. 2025 Mar 12;203(1):43. doi: 10.1007/s00408-025-00800-y.

ABSTRACT

PURPOSE: Toll-like receptor 4 (TLR4) is a transmembrane receptor promoting pro-inflammatory signalling, that is associated with the pathogenesis of pulmonary fibrosis. TLR4 is abundantly expressed on monocytes and the acceleration of TLR4 signalling induces the secretion of soluble TLR4 isoforms (sTLR4) in circulation. The aim of study was to evaluate the association of serum levels of sTLR4 with acute exacerbation (AE) and prognosis of patients with idiopathic pulmonary fibrosis (IPF).

METHODS: This retrospective cohort study included 97 patients with IPF and 76 healthy participants. The association of serum sTLR4 levels with the onset of AE and the prognosis in 97 patients with IPF was analyzed.

RESULTS: No significant difference in sTLR4 serum level was observed between the patients with IPF and healthy participants. Kaplan-Meier curves showed that patients with sTLR4 ≥ 2.2 ng/mL had a significantly higher incidence of AE-IPF and a significantly lower 5-year survival rate. Univariate and multivariate Cox hazard analyses demonstrated that sTLR4 ≥ 2.2 ng/mL was significantly associated with higher incidence of AE and poorer survival. In an exploratory analysis, a weak correlation was observed between sTLR4 levels and monocyte counts, and the incidence of AE-IPF was the highest in the patients with sTLR4 ≥ 2.2 ng/mL and monocyte counts ≥ 381/μL.

CONCLUSION: High sTLR4 level is associated with an increased incidence of AE-IPF and poor prognosis in patients with IPF. The combination of sTLR4 level and monocyte count might be used to stratify patients with IPF according to the risk for AE via reflecting monocyte activation.

PMID:40074958 | DOI:10.1007/s00408-025-00800-y

J Chem Theory Comput. 2025 Mar 13. doi: 10.1021/acs.jctc.4c01774. Online ahead of print.

ABSTRACT

Protein evolution has shaped enzymes that maintain stability and function across diverse thermal environments. While sequence variation, thermal stability and conformational dynamics are known to influence an enzyme's thermal adaptation, how these factors collectively govern stability and function across diverse temperatures remains unresolved. Cytosolic malate dehydrogenase (cMDH), a citric acid cycle enzyme, is an ideal model for studying these mechanisms due to its temperature-sensitive flexibility and broad presence in species from diverse thermal environments. In this study, we employ techniques inspired by deep learning and statistical mechanics to uncover how sequence variation and conformational dynamics shape patterns of cMDH's thermal adaptation. By integrating coevolutionary models with variational autoencoders (VAE), we generate a latent generative landscape (LGL) of the cMDH sequence space, enabling us to explore mutational pathways and predict fitness using direct coupling analysis (DCA). Structure predictions via AlphaFold and molecular dynamics simulations further illuminate how variations in hydrophobic interactions and conformational flexibility contribute to the thermal stability of warm- and cold-adapted cMDH orthologs. Notably, we identify the ratio of hydrophobic contacts between two regions as a predictive order parameter for thermal stability features, providing a quantitative metric for understanding cMDH dynamics across temperatures. The integrative computational framework employed in this study provides mechanistic insights into protein adaptation at both sequence and structural levels, offering unique perspectives on the evolution of thermal stability and creating avenues for the rational design of proteins with optimized thermal properties.

PMID:40079215 | DOI:10.1021/acs.jctc.4c01774

OMICS. 2025 Mar 12. doi: 10.1089/omi.2024.0196. Online ahead of print.

ABSTRACT

Intracellular calcium signaling is a cornerstone in cell biology and a key molecular target for human health and disease. Calcium/calmodulin dependent protein kinase kinases, CAMKK1 and CAMKK2 are serine/threonine kinases that contribute to the regulation of intracellular calcium signals in response to diverse stimuli. CAMKK1 generally has stable dynamics, whereas CAMKK2 dysregulation triggers oncogenicity and neurological disorders. To differentiate the phosphosignaling hierarchy associated with predominant phosphosites of CAMKK1 and CAMKK2, we assembled and analyzed the global cellular phosphoproteome datasets. We found that predominant phosphosites in CAMKK1 and CAMKK2 are located outside the kinase domain, and their phosphomotifs are highly homologous. Further, we employed a coregulation analysis approach to these predominant phosphosites, to infer the co-occurrence patterns of phosphorylations within CAMKKs and the coregulation patterns of other protein phosphosites with CAMKK sites. We report herein that independent phosphorylations at CAMKK2 S100 and S511 increase their enzymatic activity in the presence of calcium/calmodulin. In addition, the study unveils kinase-substrate associations such as RPS6KB1 as a novel high-confidence upstream kinase of both CAMKK1 S74 and CAMKK2 S100. Further, CAMKK2 was identified as a primary orchestrator in mediating intracellular calcium signaling cascades compared to CAMKK1 based on coregulation patterns of phosphosites from proteins involved in the calcium signaling pathway. These molecular details shed promising insights into the pathophysiology of several diseases such as cancers and psychiatric disorders associated with kinase activity dysregulations of CAMKK2 and further open the avenue for novel PTM-directed therapeutic strategies to regulate CAMKK2.

PMID:40079160 | DOI:10.1089/omi.2024.0196

Biosaf Health. 2024 Jul 14;6(5):310-318. doi: 10.1016/j.bsheal.2024.07.005. eCollection 2024 Oct.

ABSTRACT

Biosafety hazards can trigger a host immune response after infection, invasion, or contact with the host. Whether infection with a microorganism results in disease or biosafety concerns depends to a large extent on the immune status of the population. Therefore, it is essential to investigate the immunological characteristics of the host and the mechanisms of biological threats and agents to protect the host more effectively. Emerging and re-emerging infectious diseases, such as the current coronavirus disease 2019 (COVID-19) pandemic, have raised concerns regarding both biosafety and immunology worldwide. Interdisciplinary studies involved in biosafety and immunology are relevant in many fields, including the development of vaccines and other immune interventions such as monoclonal antibodies and T-cells, herd immunity (or population-level barrier immunity), immunopathology, and multispecies immunity, i.e., animals and even plants. Meanwhile, advances in immunological science and technology are occurring rapidly, resulting in important research achievements that may contribute to the recognition of emerging biosafety hazards, as well as early warning, prevention, and defense systems. This review provides an overview of the interdisciplinary field of biosafety and immunology. Close collaboration and innovative application of immunology in the field of biosafety is becoming essential for human health.

PMID:40078733 | PMC:PMC11894974 | DOI:10.1016/j.bsheal.2024.07.005

Bioinform Adv. 2025 Mar 8;5(1):vbaf013. doi: 10.1093/bioadv/vbaf013. eCollection 2025.

ABSTRACT

MOTIVATION: Epigenetic assays using next-generation sequencing have furthered our understanding of the functional genomic regions and the mechanisms of gene regulation. However, a single assay produces billions of data points, with limited information about the biological process due to numerous sources of technical and biological noise. To draw biological conclusions, numerous specialized algorithms have been proposed to summarize the data into higher-order patterns, such as peak calling and the discovery of differentially methylated regions. The key principle underlying these approaches is the search for locally consistent patterns.

RESULTS: We propose L 0 segmentation as a universal framework for extracting locally coherent signals for diverse epigenetic sources. L 0 serves to compress the input signal by approximating it as a piecewise constant. We implement a highly scalable L 0 segmentation with additional loss functions designed for sequencing epigenetic data types including Poisson loss for single tracks and binomial loss for methylation/coverage data. We show that the L 0 segmentation approach retains the salient features of the data yet can identify subtle features, such as transcription end sites, missed by other analytic approaches.

AVAILABILITY AND IMPLEMENTATION: Our approach is implemented as an R package "l01segmentation" with a C++ backend. Available at https://github.com/boooooogey/l01segmentation.

PMID:40078573 | PMC:PMC11897706 | DOI:10.1093/bioadv/vbaf013

Front Microbiol. 2025 Feb 26;16:1544521. doi: 10.3389/fmicb.2025.1544521. eCollection 2025.

ABSTRACT

Fusarium wilt caused by Fusarium oxysporum f. sp. medicaginis (Fom) is an important disease affecting lucerne/alfalfa cultivations worldwide. Medicago sativa L. (lucerne) is one of the major legume crops in global forage industry. This study aimed to identify bacteria capable of biologically controlling the wilt pathogen through a comprehensive screening of bacterial isolates obtained from domesticated and wild growing Medicago seeds. Using a multi-tiered evaluation pipeline, including in vitro, soil-free and potting mix-based pathogenicity and bioprotection assay systems, the bioprotection efficacy of 34 bacterial isolates derived from Medicago seeds was initially evaluated against six Fusarium strains in vitro. Fusarium oxysporum (Fo) F5189, which has previously been characterized as a Fusarium oxysporum f. sp. medicaginis isolate causing Fusarium wilt in lucerne was selected for in planta assays. Lucerne cultivars Grazer and Sequel, representing susceptible and resistant genotypes were chosen to assess the disease progression. Pathogenicity and bioprotection time-course studies were conducted to understand the temporal dynamics of host-pathogen interactions and efficacy of the bioprotectants. The disease symptoms were scored using a disease rating index developed in this study. The results indicated variability in bioprotection efficacy across bacterial isolates, with some strains suppressing disease in both soil-free and potting mix-based systems. Paenibacillus sp. (Lu_MgY_007; NCBI: PQ756884) and Pseudomonas sp. (Lu_LA164_018; NCBI: PQ756887) were identified as promising bioprotectants against Fusarium wilt under tested growth conditions. The time-course studies highlighted the critical role of persistent biocontrol activity and precise timing of biocontrol application for achieving long-term disease suppression. Overall, the observed reduction in disease severity underscores the potential of these bioprotectants as sustainable strategies for managing Fusarium wilt in lucerne cultivars. However, comprehensive molecular-level analyses are warranted to elucidate the underlying pathogenicity and bioprotection mechanisms, offering valuable insights for the development of more precise and effective future biocontrol strategies in agricultural systems.

PMID:40078546 | PMC:PMC11897269 | DOI:10.3389/fmicb.2025.1544521

Biosaf Health. 2023 Dec 30;6(1):21-27. doi: 10.1016/j.bsheal.2023.12.005. eCollection 2024 Feb.

ABSTRACT

Monkeypox (mpox) outbreak in 2022 has caused more than 91,000 cases, has spread to 115 countries, regions, and territories, and has thus attracted much attention. The stability of poxvirus particles in the environment is recognized as an important factor in determining their transmission. However, few studies have investigated the persistence of poxviruses on material surfaces under various environmental conditions, and their sensitivity to biocides. Here, we systematically measured the stability of vaccinia virus (VACV) under different environmental conditions and sensitivity to inactivation methods via plaque assay, quantitative real-time polymerase chain reaction (qPCR), and Gaussia luciferase (G-luciferase) reporter system. The results show that VACV is stable on the surface of stainless steel, glass, clothing, plastic, towel, A4 paper, and tissue and persists much longer at 4 °C and -20 °C, but is effectively inactivated by ultraviolet (UV) irradiation, heat treatment, and chemical reagents. Our study raises the awareness of long persistence of poxviruses in the environment and provides a simple solution to inactivate poxviruses using common disinfectants, which is expected to help the control and prevention of mpox virus and future poxvirus outbreaks.

PMID:40078309 | PMC:PMC11895014 | DOI:10.1016/j.bsheal.2023.12.005