Int J Retina Vitreous. 2025 Feb 7;11(1):14. doi: 10.1186/s40942-025-00634-z.

ABSTRACT

BACKGROUND: To illustrate the treatment effect of Pegcetacoplan for atrophy secondary to age-related macular degeneration (AMD), on an individualized topographic progression prediction basis, using a deep learning model.

METHODS: Patients (N = 99) with atrophy secondary to AMD with longitudinal optical coherence tomography (OCT) data were retrospectively analyzed. We used a previously published deep-learning-based atrophy progression prediction algorithm to predict the 2-year atrophy progression, including the topographic likelihood of future retinal pigment epithelial and outer retinal atrophy (RORA), according to the baseline OCT input. The algorithm output was a step-less individualized topographic modeling of the RORA growth, allowing for illustrating the progression line corresponding to an 80% growth compared to the natural course of 100% growth.

RESULTS: The treatment effect of Pegcetacoplan was illustrated as the line when 80% of the growth is reached in this continuous model. Besides the well-known variability of atrophy growth rate, our results showed unequal growth according to the fundus location. It became evident that this difference is of potential functional interest for patient outcomes.

CONCLUSIONS: This model based on an 80% growth of RORA after two years illustrates the variable effect of treatment with Pegcetacoplan according to the individual situation, supporting personalized medical care.

PMID:39920843 | DOI:10.1186/s40942-025-00634-z

J Neuroinflammation. 2025 Feb 7;22(1):33. doi: 10.1186/s12974-025-03356-z.

ABSTRACT

Infection with enterohemorrhagic E. coli (EHEC) causes severe changes in the brain leading to angiopathy, encephalopathy and microglial activation. In this study, we investigated the role of tumour necrosis factor alpha (TNF-α) for microglial activation and brain pathology using a preclinical mouse model of EHEC infection. LC-MS/MS proteomics of mice injected with a combination of Shiga toxin (Stx) and lipopolysaccharide (LPS) revealed extensive alterations of the brain proteome, in particular enrichment of pathways involved in complement activation and coagulation cascades. Inhibition of TNF-α by the drug Etanercept strongly mitigated these changes, particularly within the complement pathway, suggesting TNF-α-dependent vasodilation and endothelial injury. Analysis of microglial populations using a novel human-in-the-loop deep learning algorithm for the segmentation of microscopic imaging data indicated specific morphological changes, which were reduced to healthy condition after inhibition of TNF-α. Moreover, the Stx/LPS-mediated angiopathy was significantly attenuated by inhibition of TNF-α. Overall, our findings elucidate the critical role of TNF-α in EHEC-induced brain pathology and highlight a potential therapeutic target for mitigating neuroinflammation, microglial activation and injury associated with EHEC infection.

PMID:39920757 | DOI:10.1186/s12974-025-03356-z

Neurocrit Care. 2025 Feb 7. doi: 10.1007/s12028-025-02214-3. Online ahead of print.

ABSTRACT

BACKGROUND: Early prediction of hematoma expansion (HE) following nontraumatic intracerebral hemorrhage (ICH) may inform preemptive therapeutic interventions. We sought to identify how accurately machine learning (ML) radiomics models predict HE compared with expert clinicians using head computed tomography (HCT).

METHODS: We used data from 900 study participants with ICH enrolled in the Antihypertensive Treatment of Acute Cerebral Hemorrhage 2 Study. ML models were developed using baseline HCT images, as well as admission clinical data in a training cohort (n = 621), and their performance was evaluated in an independent test cohort (n = 279) to predict HE (defined as HE by 33% or > 6 mL at 24 h). We simultaneously surveyed expert clinicians and asked them to predict HE using the same initial HCT images and clinical data. Area under the receiver operating characteristic curve (AUC) were compared between clinician predictions, ML models using radiomic data only (a random forest classifier and a deep learning imaging model) and ML models using both radiomic and clinical data (three random forest classifier models using different feature combinations). Kappa values comparing interrater reliability among expert clinicians were calculated. The best performing model was compared with clinical predication.

RESULTS: The AUC for expert clinician prediction of HE was 0.591, with a kappa of 0.156 for interrater variability, compared with ML models using radiomic data only (a deep learning model using image input, AUC 0.680) and using both radiomic and clinical data (a random forest model, AUC 0.677). The intraclass correlation coefficient for clinical judgment and the best performing ML model was 0.47 (95% confidence interval 0.23-0.75).

CONCLUSIONS: We introduced supervised ML algorithms demonstrating that HE prediction may outperform practicing clinicians. Despite overall moderate AUCs, our results set a new relative benchmark for performance in these tasks that even expert clinicians find challenging. These results emphasize the need for continued improvements and further enhanced clinical decision support to optimally manage patients with ICH.

PMID:39920546 | DOI:10.1007/s12028-025-02214-3

EJNMMI Phys. 2025 Feb 8;12(1):15. doi: 10.1186/s40658-025-00716-9.

ABSTRACT

PURPOSE: Whole-brain segmentation via positron emission tomography (PET) imaging is crucial for advancing neuroscience research and clinical medicine, providing essential insights into biological metabolism and activity within different brain regions. However, the low resolution of PET images may have limited the segmentation accuracy of multiple brain structures. Therefore, we propose a generative multi-object segmentation model for brain PET images to achieve automatic and accurate segmentation.

METHODS: In this study, we propose a generative multi-object segmentation model for brain PET images with two learning protocols. First, we pretrained a latent mapping model to learn the mapping relationship between PET and MR images so that we could extract anatomical information of the brain. A 3D multi-object segmentation model was subsequently proposed to apply whole-brain segmentation to MR images generated from integrated latent mapping models. Moreover, a custom cross-attention module based on a cross-attention mechanism was constructed to effectively fuse the functional information and structural information. The proposed method was compared with various deep learning-based approaches in terms of the Dice similarity coefficient, Jaccard index, precision, and recall serving as evaluation metrics.

RESULTS: Experiments were conducted on real brain PET/MR images from 120 patients. Both visual and quantitative results indicate that our method outperforms the other comparison approaches, achieving 75.53% ± 4.26% Dice, 66.02% ± 4.55% Jaccard, 74.64% ± 4.15% recall and 81.40% ± 2.30% precision. Furthermore, the evaluation of the SUV distribution and correlation assessment in the regions of interest demonstrated consistency with the ground truth. Additionally, clinical tolerance rates, which are determined by the tumor background ratio, have confirmed the ability of the method to distinguish highly metabolic regions accurately from normal regions, reinforcing its clinical applicability.

CONCLUSION: For automatic and accurate whole-brain segmentation, we propose a novel 3D generative multi-object segmentation model for brain PET images, which achieves superior model performance compared with other deep learning methods. In the future, we will apply our whole-brain segmentation method to clinical practice and extend it to other multimodal tasks.

PMID:39920478 | DOI:10.1186/s40658-025-00716-9

Discov Ment Health. 2025 Feb 8;5(1):12. doi: 10.1007/s44192-025-00138-0.

ABSTRACT

INTRODUCTION: Perinatal mental disorders are prevalent, affecting 10-20% of pregnant women, and can negatively impact both maternal and neonatal outcomes. Traditional screening tools, such as the Edinburgh Postnatal Depression Scale (EPDS), present limitations due to subjectivity and time constraints in clinical settings. Recent advances in voice analysis and machine learning have shown potential for providing more objective screening methods. This study aimed to develop a deep learning model that analyzes the voices of pregnant women to screen for mental disorders, thereby offering an alternative to the traditional tools.

METHODS: A cross-sectional study was conducted among 204 pregnant women, from whom voice samples were collected during their one-month postpartum checkup. The audio data were preprocessed into 5000 ms intervals, converted into mel-spectrograms, and augmented using TrivialAugment and context-rich minority oversampling. The EfficientFormer V2-L model, pretrained on ImageNet, was employed with transfer learning for classification. The hyperparameters were optimized using Optuna, and an ensemble learning approach was used for the final predictions. The model's performance was compared to that of the EPDS in terms of sensitivity, specificity, and other diagnostic metrics.

RESULTS: Of the 172 participants analyzed (149 without mental disorders and 23 with mental disorders), the voice-based model demonstrated a sensitivity of 1.00 and a recall of 0.82, outperforming the EPDS in these areas. However, the EPDS exhibited higher specificity (0.97) and precision (0.84). No significant difference was observed in the area under the receiver operating characteristic curve between the two methods (p = 0.759).

DISCUSSION: The voice-based model showed higher sensitivity and recall, suggesting that it may be more effective in identifying at-risk individuals than the EPDS. Machine learning and voice analysis are promising objective screening methods for mental disorders during pregnancy, potentially improving early detection.

CONCLUSION: We developed a lightweight machine learning model to analyze pregnant women's voices for screening various mental disorders, achieving high sensitivity and demonstrating the potential of voice analysis as an effective and objective tool in perinatal mental health care.

PMID:39920468 | DOI:10.1007/s44192-025-00138-0

Int J Comput Assist Radiol Surg. 2025 Feb 7. doi: 10.1007/s11548-025-03329-w. Online ahead of print.

ABSTRACT

PURPOSE: In recent years, the detection of the duodenal papilla and surgical cannula has become a critical task in computer-assisted endoscopic retrograde cholangiopancreatography (ERCP) cannulation operations. The complex surgical anatomy, coupled with the small size of the duodenal papillary orifice and its high similarity to the background, poses significant challenges to effective computer-assisted cannulation. To address these challenges, we present a deep learning-driven graphical user interface (GUI) to assist ERCP cannulation.

METHODS: Considering the characteristics of the ERCP scenario, we propose a deep learning method for duodenal papilla and surgical cannula detection, utilizing four swin transformer decoupled heads (4STDH). Four different prediction heads are employed to detect objects of different sizes. Subsequently, we integrate the swin transformer module to identify attention regions to explore prediction potential deeply. Moreover, we decouple the classification and regression networks, significantly improving the model's accuracy and robustness through the separation prediction. Simultaneously, we introduce a dataset on papilla and cannula (DPAC), consisting of 1840 annotated endoscopic images, which will be publicly available. We integrated 4STDH and several state-of-the-art methods into the GUI and compared them.

RESULTS: On the DPAC dataset, 4STDH outperforms state-of-the-art methods with an mAP of 93.2% and superior generalization performance. Additionally, the GUI provides real-time positions of the papilla and cannula, along with the planar distance and direction required for the cannula to reach the cannulation position.

CONCLUSION: We validate the GUI's performance in human gastrointestinal endoscopic videos, showing deep learning's potential to enhance the safety and efficiency of clinical ERCP cannulation.

PMID:39920403 | DOI:10.1007/s11548-025-03329-w

Eur Spine J. 2025 Feb 8. doi: 10.1007/s00586-025-08668-5. Online ahead of print.

ABSTRACT

BACKGROUND: For cases of multilevel lumbar disc herniation (LDH), selecting the surgical approach for Percutaneous Transforaminal Endoscopic Discectomy (PTED) presents significant challenges and heavily relies on the physician's judgment. This study aims to develop a deep learning (DL)-based multimodal model that provides objective and referenceable support by comprehensively analyzing imaging and clinical data to assist physicians.

METHODS: This retrospective study collected imaging and clinical data from patients with multilevel LDH. Each segmental MR scan was concurrently fed into a multi-input ResNet 50 model to predict the target segment. The target segment scan was then input to a custom model to predict the PTED approach direction. Clinical data, including the patient's lower limb sensory and motor functions, were used as feature variables in a machine learning (ML) model for prediction. Bayesian optimization was employed to determine the optimal weights for the fusion of the two models.

RESULT: The predictive performance of the multimodal model significantly outperformed the DL and ML models. For PTED target segment prediction, the multimodal model achieved an accuracy of 93.8%, while the DL and ML models achieved accuracies of 87.7% and 87.0%, respectively. Regarding the PTED approach direction, the multimodal model had an accuracy of 89.3%, significantly higher than the DL model's 87.8% and the ML model's 87.6%.

CONCLUSION: The multimodal model demonstrated excellent performance in predicting PTED target segments and approach directions. Its predictive performance surpassed that of the individual DL and ML models.

PMID:39920320 | DOI:10.1007/s00586-025-08668-5

Eur Spine J. 2025 Feb 8. doi: 10.1007/s00586-025-08696-1. Online ahead of print.

ABSTRACT

PURPOSE: This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) to differentiate between tuberculous spondylitis (TS) and pyogenic spondylitis (PS) using contrast-enhanced MRI (CE-MRI).

METHODS: A retrospective approach was employed, enrolling patients diagnosed with TS or PS based on pathological examination at two centers. Clinical features were evaluated to establish a clinical model. Radiomics and deep learning (DL) features were extracted from contrast-enhanced T1-weighted images and subsequently fused. Following feature selection, radiomics, DL, combined DL-radiomics (DLR), and a deep learning radiomics nomogram (DLRN) were developed to differentiate TS from PS. Performance was assessed using metrics including the area under the curve (AUC), calibration curves, and decision curve analysis (DCA).

RESULTS: A total of 147 patients met the study criteria. Center 1 comprised the training cohort with 102 patients (52 TS and 50 PS), while Center 2 served as the external test cohort with 45 patients (17 TS and 28 PS). The DLRN model exhibited the highest diagnostic accuracy, achieving an AUC of 0.994 (95% CI: 0.983-1.000) in the training cohort and 0.859 (95% CI: 0.744-0.975) in the external test cohort. Calibration curves indicated good agreement for DLRN, and decision curve analysis (DCA) demonstrated it provided the greatest clinical benefit.

CONCLUSION: The CE-MRI-based DLRN showed robust diagnostic capability for distinguishing between TS and PS in clinical practice.

PMID:39920318 | DOI:10.1007/s00586-025-08696-1

Sci Rep. 2025 Feb 7;15(1):4594. doi: 10.1038/s41598-025-88658-1.

ABSTRACT

The influence of deep learning in the medical and molecular biology sectors is swiftly growing and holds the potential to improve numerous crucial domains. Osteoporosis is a significant global health issue, and the current treatment options are highly restricted. Transplanting genetically engineered MSCs has been acknowledged as a highly promising therapy for osteoporosis. We utilized a random walk-based technique to discern genes associated with ossification. The osteogenic value of these genes was assessed on the basis of information found in published scientific literature. GO enrichment analysis of these genes was performed to determine if they were enriched in any certain function. Immunohistochemical and western blot techniques were used to identify and measure protein expression. The expression of genes involved in osteogenic differentiation was examined via qRT‒PCR. Lentiviral transfection was utilized to suppress the expression of the FOXA1 gene in hBMSCs. An in vivo mouse model of ovariectomy was created, and radiographic examination was conducted to confirm the impact of FOXA1 knockdown on osteoporosis. The osteogenic score of each gene was calculated by assessing its similarity to osteo-specific genes. The majority of the genes with the highest rankings were linked with osteogenic differentiation, indicating that our approach is useful for identifying genes associated with ossification. GO enrichment analysis revealed that these pathways are enriched primarily in bone-related processes. FOXA1 is a crucial transcription factor that controls the process of osteogenic differentiation, as indicated by similarity analysis. FOXA1 was significantly increased in those with osteoporosis. Downregulation of FOXA1 markedly augmented the expression of osteoblast-specific genes and proteins, activated the ERK1/2 signaling pathway, intensified ALP activity, and promoted mineral deposition. In addition, excessive expression of FOXA1 significantly reduced ALP activity and mineral deposits. Using a mouse model in which the ovaries were surgically removed, researchers reported that suppressing the FOXA1 gene in bone marrow stem cells (BMSCs) prevented the loss of bone density caused by ovariectomy. This finding was confirmed by analyzing the bone structure via micro-CT. Furthermore, our approach can distinguish genes that exhibit osteogenic differentiation characteristics. This ability can aid in the identification of novel genes associated with osteogenic differentiation, which can be utilized in the treatment of osteoporosis. Computational and laboratory evidence indicates that reducing the expression of FOXA1 enhances the process of bone formation in bone marrow-derived mesenchymal stem cells (BMSCs) and may serve as a promising approach to prevent osteoporosis.

PMID:39920313 | DOI:10.1038/s41598-025-88658-1

Sci Rep. 2025 Feb 7;15(1):4620. doi: 10.1038/s41598-025-87830-x.

ABSTRACT

Symmetric coin cell cycling is an important tool for the analysis of battery materials, enabling the study of electrode/electrolyte systems under realistic operating conditions. In the case of metal lithium SEI growth and shape changes, cycling studies are especially important to assess the impact of the alternation of anodic-cathodic polarization with the relevant electrolyte geometry and mass-transport conditions. Notwithstanding notable progress in analysis of lithium/lithium symmetric coin cell cycling data, on the one hand, some aspects of the cell electrochemical response still warrant investigation, and, on the other hand, very limited quantitative use is made of large corpora of experimental data generated in electrochemical experiments. This study contributes to shedding light on this highly technologically relevant problem, thanks to the combination of quantitative data exploitation and Partial Differential Equation (PDE) modelling for metal anode cycling. Toward this goal, we propose the use of a Convolutional Neural Network-Long-Short Term Memory (CNN-LSTM) to identify relevant physico-chemical parameters in the PDE model and to describe the behaviour of simulated and experimental charge-discharge profiles. Specifically, we have carried out parameter identification tasks for experimental data regarding the cycling of symmetric coin cells with Li chips as electrodes and LP30 electrolyte. Representative selection of numerical results highlights the advantages of this new approach with respect to traditional Least Squares fitting.

PMID:39920238 | DOI:10.1038/s41598-025-87830-x

Cell Commun Signal. 2025 Feb 7;23(1):67. doi: 10.1186/s12964-025-02076-4.

ABSTRACT

Synchrotron-radiation nano-X-Ray Fluorescence (XRF) is a cutting-edge technique offering high-resolution insights into the elemental composition of biological tissues, shedding light on metabolic processes and element localization within cellular structures. In the context of Idiopathic Pulmonary Fibrosis (IPF), a debilitating lung condition associated with respiratory complications and reduced life expectancy, nano-XRF presents a promising avenue for understanding the disease's intricate pathology. Our developed workflow enables the assessment of elemental composition in both human and rodent fibrotic tissues, providing insights on the interplay between cellular compartments in chronic lung diseases. Our findings demonstrate trace element accumulations associated with anthracosis, a feature observed in IPF. Notably, Zn and Ca clusters approximately 750 nm in size were identified exclusively in IPF samples. While their specific role remains unclear, their presence may be associated with disease-specific processes. Additionally, we observed Fe and S signal colocalization in 650-nm structures within some IPF cells. Fe-S complexes in mitochondria are known to be associated with increased ROS production, suggesting a potential connection to the disease pathology. In contrast, a bleomycin-induced fibrosis rodent model exhibits a different elemental phenotype with low Fe and increased S, Zn, and Ca. Overall, our workflow highlights the effectiveness of synchrotron-based nano-XRF mapping in analyzing the spatial distribution of trace elements within diseased tissue, offering valuable insights into the elemental aspects of IPF and related chronic lung diseases.

PMID:39920750 | DOI:10.1186/s12964-025-02076-4

Cell Biol Toxicol. 2025 Feb 7;41(1):40. doi: 10.1007/s10565-025-09990-w.

ABSTRACT

Autophagy related genes (ATGs) play essential roles in maintaining cellular functions, although biological and pathological alterations of ATG phenotypes remain poorly understood. To address this knowledge gap, we utilized the single-cell sequencing technology to elucidate the transcriptomic atlas of ATGs in lung diseases, with a focus on lung epithelium and lymphocytes. This study conducted a comprehensive investigation into RNA profiles of ATGs in the lung tissues obtained from healthy subjects and patients with different lung diseases through single-cell RNA sequencing (scRNA-seq), including COVID-19 related acute lung damage, idiopathic pulmonary fibrosis (IPF), chronic obstructive pulmonary disease (COPD), systemic sclerosis (SSC), and lung adenocarcinoma (LUAD). Our findings revealed significant variations of ATGs expression across lung epithelial cell subsets, e.g., over-expression of MAPK8 in basal cells, ATG10 in club cells, and BCL2 in a goblet cell subset. The changes of autophagy-related pathways varied between lung epithelial and lymphocyte subsets. We identified the disease-associated changes in ATG expression, including significant alterations in BCL2, BCL2L1, PRKCD, and PRKCQ in inflammatory lung diseases (COPD and IPF), and MAP2K7, MAPK3, and RHEB in lung cancer (LUAD), as compared to normal lung tissues. Key ligand-receptor pairs (e.g., CD6-ALCAM, CD99-CD99) and signaling pathways (e.g., APP, CD74) might serve as biomarkers for lung diseases. To evaluate ATGs responses to external challenges, we examined ATGs expression in different epithelial cell lines exposed to cigarette smoking extract (CSE), lysophosphatidylcholine (lysoPC), lipopolysaccharide (LPS), and cholesterol at various doses and durations. Notable changes were observed in CFLAR, EIF2S1, PPP2CA, and PPP2CB in A549 and H1299 against CSE and LPS. The heterogeneity of ATGs expression was dependent on cell subsets, pathologic conditions, and challenges, as well as varied among cellular phenotypes, functions, and behaviors, and the severity of lung diseases. In conclusion, our data might provide new insights into the roles of ATGs in epithelial biology and pulmonary disease pathogenesis, with implications for disease progression and prognosis.

PMID:39920481 | DOI:10.1007/s10565-025-09990-w

Respirology. 2025 Feb 7. doi: 10.1111/resp.14894. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with a poor prognosis. Antifibrotics slow the decline of pulmonary function after 12-months, but limited studies have examined the role of circulatory biomarkers in antifibrotic treated IPF patients.

METHODS: Serum from 98 IPF participants, from the Australian Idiopathic Pulmonary Fibrosis Registry were collected at four time-points over 1 year post-antifibrotic treatment and analysed as two separate cohorts. Patients were stratified as progressive, if they experienced ≥ 10% decline in FVC or ≥ 15% decline in DLCO or were deceased within 1 year of treatment initiation: or otherwise as stable. Ten molecules of interest were measured by ELISAs in patient serum.

RESULTS: Baseline MMP7 levels were higher in progressive than stable patients in Cohort 1 (p = 0.02) and Cohort 2 (p = 0.0002). Baseline MMP7 levels also best differentiated progressive from stable patients (Cohort 1, AUC = 0.74, p = 0.02; Cohort 2, AUC = 0.81, p = 0.0003). Regression analysis of the combined cohort showed that elevated MMP7 levels predicted 12-month progression (OR = 1.530, p = 0.010) and increased risk of overall mortality (HR = 1.268, p = 0.002). LASSO regression identified a multi-biomarker panel (MMP7, ICAM-1, CHI3L1, CA125) that differentiated progression more accurately than MMP7 alone. Furthermore, GAP combined with MMP7, ICAM-1, CCL18 and SP-D was more predictive of 3-year mortality than GAP alone.

CONCLUSION: MMP7 along with a multi-biomarker and GAP panel can predict IPF progression and mortality, with the potential for optimising management.

PMID:39919729 | DOI:10.1111/resp.14894

Microbiome. 2025 Feb 7;13(1):46. doi: 10.1186/s40168-025-02043-8.

ABSTRACT

BACKGROUND: Neonatal mice are frequently used to model diseases that affect human infants. Microbial community composition has been shown to impact disease progression in these models. Despite this, the maturation of the early-life murine microbiome has not been well-characterized. We address this gap by characterizing the assembly of the bacterial microbiota of C57BL/6 and BALB/c litters from birth to adulthood across multiple independent litters.

RESULTS: The fecal microbiome of young pups is dominated by only a few pioneering bacterial taxa. These taxa are present at low levels in the microbiota of multiple maternal body sites, precluding a clear identification of maternal source. The pup microbiota begins diversifying after 14 days, coinciding with the beginning of coprophagy and the consumption of solid foods. Pup stool bacterial community composition and diversity are not significantly different from dams from day 21 onwards. Short-read shotgun sequencing-based metagenomic profiling of young pups enabled the assembly of metagenome-assembled genomes for strain-level analysis of these pioneer Ligilactobacillus, Streptococcus, and Proteus species.

CONCLUSIONS: Assembly of the murine microbiome occurs over the first weeks of postnatal life and is largely complete by day 21. This detailed view of bacterial community development across multiple commonly employed mouse strains informs experimental design, allowing researchers to better target interventions before, during, or after the maturation of the bacterial microbiota. The source of pioneer bacterial strains appears heterogeneous, as the most abundant taxa identified in young pup stool were found at low levels across multiple maternal body sites, suggesting diverse routes for seeding of the murine microbiome. Video Abstract.

PMID:39920864 | DOI:10.1186/s40168-025-02043-8

Genome Med. 2025 Feb 7;17(1):11. doi: 10.1186/s13073-025-01435-7.

ABSTRACT

Ineffective medication is a major healthcare problem causing significant patient suffering and economic costs. This issue stems from the complex nature of diseases, which involve altered interactions among thousands of genes across multiple cell types and organs. Disease progression can vary between patients and over time, influenced by genetic and environmental factors. To address this challenge, digital twins have emerged as a promising approach, which have led to international initiatives aiming at clinical implementations. Digital twins are virtual representations of health and disease processes that can integrate real-time data and simulations to predict, prevent, and personalize treatments. Early clinical applications of DTs have shown potential in areas like artificial organs, cancer, cardiology, and hospital workflow optimization. However, widespread implementation faces several challenges: (1) characterizing dynamic molecular changes across multiple biological scales; (2) developing computational methods to integrate data into DTs; (3) prioritizing disease mechanisms and therapeutic targets; (4) creating interoperable DT systems that can learn from each other; (5) designing user-friendly interfaces for patients and clinicians; (6) scaling DT technology globally for equitable healthcare access; (7) addressing ethical, regulatory, and financial considerations. Overcoming these hurdles could pave the way for more predictive, preventive, and personalized medicine, potentially transforming healthcare delivery and improving patient outcomes.

PMID:39920778 | DOI:10.1186/s13073-025-01435-7

J Transl Med. 2025 Feb 7;23(1):166. doi: 10.1186/s12967-025-06180-4.

ABSTRACT

Prostate cancer (PCa) is a kind of malignant solid tumor commonly observed among males worldwide. The dilemma of increasing incidence with therapeutic resistance has become the leading issue in PCa clinical management. Ferroptosis is a new form of regulatory cell death caused by iron-dependent lipid peroxidation, which has a dual role in PCa evolution and treatment due to the multi-omics cascade of interactions among pathways and environmental stimuli. Hence deciphering the role of ferroptosis in carcinogenesis would provide novel insights and strategies for precision medicine and personalized healthcare against PCa. In this study, the mechanisms of ferroptosis during cancer development were summarized both at the molecular and tumor microenvironment level. Then literature-reported ferroptosis-related signatures in PCa, e.g., genes, non-coding RNAs, metabolites, natural products and drug components, were manually collected and functionally compared as drivers/inducers, suppressors/inhibitors, and biomarkers according to their regulatory patterns in PCa ferroptosis and pathogenesis. The state-of-the-art techniques for ferroptosis-related data integration, knowledge identification, and translational application to PCa theranostics were discussed from a combinative perspective of artificial intelligence-powered modelling and advanced material-oriented therapeutic scheme design. The prospects and challenges in ferroptosis-based PCa researches were finally highlighted to light up future wisdoms for the flourishing of current findings from bench to bedside.

PMID:39920771 | DOI:10.1186/s12967-025-06180-4

BMC Cancer. 2025 Feb 7;25(1):217. doi: 10.1186/s12885-025-13616-z.

ABSTRACT

BACKGROUND: The relationship between cancer development and alterations in IgG N-glycosylation has been well-established. However, comprehensive profiling of the N-glycome and N-glycoproteome in gastric cancer (GC) remains limited. Furthermore, the prognostic potential of IgG N-glycan patterns in identifying precursors to GC has yet to be fully elucidated.

METHODS: The IgG N-glycome in GC was characterized using a custom high-throughput orthogonal mass spectrometry approach. Multivariate analysis was employed to identify and assess glycomic alterations. A comprehensive bioinformatics analysis was also conducted to investigate the differential expression of N-glycosylation-related genes and their potential roles in GC pathogenesis. Additionally, interleukin-11 (IL-11) levels were quantified using a standardized enzyme-linked immunosorbent assay (ELISA).

RESULTS: Galactosylation and sialylation of IgG decreased mainly in the IgG1 and IgG2 subclasses in GC, with subclass-specific changes in IgG3 and IgG4 galactosylation. These glycan modifications were represented by unique glycopeptides (IgG1_H5N5, IgG2_H4N3F1, IgG2_H4N4, IgG2_H4N4F1S1, IgG3/4_H4N4F1, IgG3/4_H4N4F1S1), which outperformed CA72-4 for GC diagnosis. Analysis of key glycogenes revealed differential expression patterns, implicating a functional role for IgG N-glycosylation in GC. Notably, the abundance of specific IgG glycosylation exhibited a significant correlation with serum level of IL-11.

CONCLUSIONS: Alterations in subclass-specific IgG N-glycosylation represent promising biomarkers for the detection and monitoring of GC progression, potentially influenced by cytokine-driven inflammation. Understanding these changes could improve our knowledge of molecular mechanisms, aiding in diagnostic improvements and therapeutic development.

PMID:39920693 | DOI:10.1186/s12885-025-13616-z

Metabolomics. 2025 Feb 7;21(1):24. doi: 10.1007/s11306-024-02220-0.

ABSTRACT

BACKGROUND: Chronic periodontitis is a condition impacting approximately 50% of the world's population. As chronic periodontitis progresses, the bacteria in the oral cavity change resulting in new microbial interactions which in turn influence metabolite production. Chronic periodontitis manifests with inflammation of the periodontal tissues, which is progressively developed due to bacterial infection and prolonged bacterial interaction with the host immune response. The bi-directional relationship between periodontitis and systemic diseases has been reported in many previous studies. Traditional diagnostic methods for chronic periodontitis and systemic diseases such as chronic kidney diseases (CKD) have limitations due to their invasiveness, requiring practised individuals for sample collection, frequent blood collection, and long waiting times for the results. More rapid methods are required to detect such systemic diseases, however, the metabolic profiles of the oral cavity first need to be determined.

AIM OF REVIEW: In this review, we explored metabolomics studies that have investigated salivary metabolic profiles associated with chronic periodontitis and systemic illnesses including CKD, oral cancer, Alzheimer's disease, Parkinsons's disease, and diabetes to highlight the most recent methodologies that have been applied in this field.

KEY SCIENTIFIC CONCEPTS OF THE REVIEW: Of the rapid, high throughput techniques for metabolite profiling, Nuclear magnetic resonance (NMR) spectroscopy was the most applied technique, followed by liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS). Furthermore, Raman spectroscopy was the most used vibrational spectroscopic technique for comparison of the saliva from periodontitis patients to healthy individuals, whilst Fourier Transform Infra-Red Spectroscopy (FT-IR) was not utilised as much in this field. A recommendation for cultivating periodontal bacteria in a synthetic medium designed to replicate the conditions and composition of saliva in the oral environment is suggested to facilitate the identification of their metabolites. This approach is instrumental in assessing the potential of these metabolites as biomarkers for systemic illnesses.

PMID:39920480 | DOI:10.1007/s11306-024-02220-0

Nat Immunol. 2025 Feb 7. doi: 10.1038/s41590-025-02099-6. Online ahead of print.

NO ABSTRACT

PMID:39920361 | DOI:10.1038/s41590-025-02099-6

Sci Rep. 2025 Feb 7;15(1):4627. doi: 10.1038/s41598-025-88168-0.

ABSTRACT

Essential oils (EO) are known for their antibacterial and anti-inflammatory properties and can be used as an alternative to reduce the reliance on antimicrobials in dairy cattle. While many studies have explored the beneficial properties of EO in vitro, their effects on milk quality and milk microbiota, when applied directly to the udder skin, remain relatively unknown. This study aimed to investigate the impact of Thymus capitatus essential oil (TCEO), known for its high antibacterial and antioxidant properties, on milk microbiota using 16S rRNA sequencing, the lipidomic profile via liquid chromatography-quadrupole time-of-flight mass spectrometry, udder skin microbiota, and inflammatory biomarkers of dairy cows at the end of lactation. Sixteen-quarters of 12 Holstein cows were selected, and TCEO was topically applied to the udder skin twice a day for 7 days. Milk was collected aseptically on days 0, 7, 21, and 28 before morning farm milking. The results showed no significant changes in microbiota composition after the EO treatment in alpha and beta diversity or taxonomical composition at the phylum and genus levels. TCEO induced limited changes in the milk lipidome, primarily affecting diacylglycerols at T21. The treatment did not affect inflammatory biomarkers, milk sensory properties, or quality. Our study is the first to demonstrate that a local application of 10% TCEO on cow's quarters does not significantly alter milk quality or microbiota composition in milk and skin. More studies should be conducted to ensure the safe use of TCEO in dairy cows and explore its potential benefits on antibiotic-resistant bacteria as an alternative or support for antibiotic therapy.

PMID:39920235 | DOI:10.1038/s41598-025-88168-0