Search Results (3,002)

The global challenge of antimicrobial resistance (AMR) has been framed primarily in terms of genetic resistance mechanisms. Nevertheless, bacteria can also survive antimicrobial stress through phenotypic plasticity, resulting in transient, non-genetic states such as tolerance, persistence, and population-level resilience. These phenotypic states complicate diagnostic efforts, diminish antibiotic efficacy, and contribute to the chronic nature of infections even in the absence of heritable resistance. This review evaluates phenotypic plasticity as a significant yet underrecognized factor in AMR, with a focus on responses to oxidative and photodynamic stress. Key manifestations of plasticity are discussed, including morphological and metabolic remodeling such as filamentation, small-colony variants, and metabolic rewiring, as well as envelope- and biofilm-associated heterogeneity and regulatory flexibility mediated by gene networks and horizontal regulatory transfer. The review highlights plastic responses elicited by reactive oxygen species-mediated stress and antimicrobial photodynamic inactivation, where single-cell heterogeneity, biofilm and mucus barriers, and light-dependent cues influence bacterial survival. Case studies are presented to demonstrate how photodynamic strategies can induce transient protective states and act synergistically with antibiotics, revealing mechanisms of action that extend beyond conventional single-target therapeutic models. Drawing on evidence from single-cell analyses, biofilm ecology, and experimental evolution, this review establishes phenotypic plasticity as a central element in the chemical biology of AMR. Enhanced understanding of plasticity is essential for advancing diagnostics, informing the development of adjuvant therapies, and predicting bacterial responses to novel antimicrobial interventions. Full article

Immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 and CTLA-4 are widely used in the treatment of several cancers and have significantly improved survival outcomes in responsive patients. However, a substantial proportion of patients fail to benefit from these therapies, underscoring the urgent need for accurate prediction of ICI response. We propose a deep learning framework, ICIsc, to accurately predict ICI response by integrating single-cell RNA sequencing (scRNA-seq) data with protein large language models. Specifically, patient representations are constructed using transcriptomic profiles and immune-related gene set scores as latent embedding features, while drug representations are derived from amino acid sequences of ICI encoded by the Evolutionary Scale Modeling 2 (ESM2). For bulk data, ICIsc employs a bilinear attention module to fuse patient and drug embeddings for response prediction. For scRNA-seq data, ICIsc infers cell–cell interactions using a single-sample network (SSN) approach and applies GATv2 to model immune microenvironment heterogeneity at the single-cell level. Benchmark evaluations and independent validation demonstrate that ICIsc consistently outperforms baseline models and exhibits robust generalization performance. SHAP-based interpretability analysis further identifies key genes (e.g., GAPDH) associated with immunotherapy response and patient prognosis. Overall, ICIsc provides an accurate and interpretable framework for predicting immunotherapy outcomes and elucidating underlying mechanisms. Full article

Osteosarcoma (OS) is the most common primary malignant bone tumor in adolescents, and outcomes for metastatic disease have remained poor, highlighting the need for molecular biomarkers. We integrated three Gene Expression Omnibus (GEO) mRNA expression datasets (GSE12865, GSE14359, and GSE246405) to identify differentially expressed genes (DEGs) between OS and non-malignant bone-related controls. Overlapping DEGs were used to build a protein–protein interaction network, and hub genes were prioritized using multiple network topology algorithms. Prognostic associations were evaluated using the R2 Genomics Platform. Putative upstream miRNAs targeting the top candidate were obtained from prediction databases and intersected with dysregulated circulating miRNAs from GSE65071 (localized OS plasma vs. healthy controls). Functional enrichment analyses (Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and cancer hallmarks) were performed to contextualize the candidate signature. We identified 107 overlapping DEGs and prioritized eight hub genes. CD81 was significantly associated with overall survival (Bonferroni-adjusted p = 0.043) and showed reduced expression in OS tissues and cell line models. hsa-miR-582-5p was nominated as a candidate miRNA predicted to target CD81 and was upregulated in OS plasma. Enrichment results linked the signature to angiogenesis, extracellular matrix remodeling, focal adhesion, and metastasis-associated signatures. These findings support CD81 as a candidate prognostic biomarker and nominate a putative hsa-miR-582-5p–CD81 relationship for future validation. Full article

Background/Objectives: Thymomas are the most common tumors of the anterior mediastinum. While early-stage disease often has a favorable prognosis, therapeutic options in advanced stages remain limited. Moreover, the molecular profile of thymomas is still poorly characterized. In the present study, we explored the presence of targetable mutations and programmed death-ligand 1 (PD-L1) expression in a cohort of surgically resected thymomas. Furthermore, we investigated the correlation between PD-L1 expression, histological subtype, and risk of recurrence in patients who underwent curative-intent thymectomy. Methods: Mutational profiling was performed using a DNA-based NGS Cancer Panel of 16 genes. PD-L1 expression was evaluated via Tumor Proportion Score (TPS), and thymomas with TPS ≥ 50% were identified as high expressors. The associations with histological subtype and disease-free survival (DFS) were analyzed using logistic regression, Cox proportional hazards models, and Kaplan–Meier survival curves. Results: In our study, 2/37 (5.4%) of tested neoplasms (type AB and B2 thymoma) reported as a PIK3CA mutation; no other targetable mutations were observed. Moreover, high PD-L1 expression (≥50%) was reported in (15/37) 40.5% of patients and was significantly associated with aggressive histological subtypes (B2 and B3) (p < 0.001). Logistic regression analysis showed that high PD-L1 expression was a significant predictor of aggressive histology (McFadden’s R² = 0.268, p < 0.001), with an odds ratio of 15.5 (95% CI: 2.9–83.4; p = 0.001). During follow-up, 5/37 (13.5%) of patients experienced disease recurrence; however, no significant difference in DFS was found between high and low PD-L1 expression groups. Conclusions: Our data confirm the presence of PIK3CA mutations in thymomas and encourage the exploration the potential role of molecular target therapy in this setting. Moreover, we underlined that high PD-L1 expression level is associated with more aggressive thymoma subtypes and may have a role as a prognostic biomarker. These findings support the need for further studies on the potential role of molecular and predictive pathology in thymic epithelial tumors. Full article

Pseudorabies (PR) is an acute and highly contagious disease caused by the pseudorabies virus (PRV). This virus has a wide range of susceptible hosts and has caused major economic losses to the global swine industry. While rosmarinic acid possesses broad antioxidant and antiviral properties, its efficacy against PRV has remained unexplored. Therefore, this study aimed to evaluate the anti-PRV activity of rosmarinic acid and to elucidate its underlying mechanism, with a focus on the nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway. The results revealed that rosmarinic acid exhibited potent, concentration-dependent antiviral activity in vitro, with a half-maximal inhibitory concentration (IC50) of 0.02654 mg/mL, a half-maximal cytotoxic concentration (CC50) of 0.1043 mg/mL, and a selectivity index (SI) of 3.9. Rosmarinic acid inhibited virus adsorption, entry, and intracellular replication. It also significantly suppressed the expression of the gB protein. In a mouse model, rosmarinic acid treatment (200 mg/kg) significantly enhanced the survival rate to 28.5%. This treatment reduced the viral load in the brain, lungs, kidneys, heart, and spleen. It also alleviated the tissue damage caused by PRV infection. Furthermore, rosmarinic acid counteracted PRV-induced oxidative stress by elevating the activity of the antioxidant factors SOD and CAT and reducing the level of the oxidative factor MDA. Combined network pharmacology and molecular docking analyses predicted the Nrf2 signaling pathway as a key target for rosmarinic acid. Subsequent mechanistic studies confirmed that rosmarinic acid upregulated the expression of the Nrf2, HO-1, GPX, SOD, and CAT genes, as well as Nrf2 and HO-1 proteins, thereby promoting the nuclear translocation of Nrf2. These results identify rosmarinic acid as a promising anti-PRV agent that acts through multi-phase viral inhibition and activation of the Nrf2-mediated antioxidant defense, suggesting its potential as a novel pharmacological strategy against PRV. Full article

Purpose: This study aimed to investigate the association between lactate dehydrogenase-to-albumin ratio (LAR) and the clinical characteristics and overall survival (OS) of patients with neuroblastoma (NB). Methods: We conducted a retrospective data analysis of 443 patients diagnosed with neuroblastoma. The optimal cut-off value for the LAR was determined using receiver operating characteristic (ROC) curves. We utilized Kaplan–Meier curves and Cox regression analysis to evaluate the relationship between LAR and OS. Independent factors identified through multivariate analysis were employed to construct a nomogram. The performance of the nomogram model was assessed using calibration curves, ROC curves, concordance index (C-index), and decision curve analysis (DCA). Results: The 2-year time-dependent ROC curve indicated that the optimal cut-off value for the LAR was 21.814. Kaplan–Meier survival curve analysis revealed that the prognosis for the high LAR group was significantly worse than that for the low LAR group. Results from multivariate Cox analysis identified INSS stage, bone metastasis, MYCN, and LAR as independent prognostic factors for OS. A nomogram for predicting the prognosis of NB was established based on multivariate Cox regression analysis. Internal validation through the Bootstrap method revealed that the nomogram’s C-index was 0.727. Both the calibration curve and ROC curve suggested that the model possessed significant predictive potential. DCA further demonstrated that the nomogram model exhibited substantial clinical applicability. Conclusions: LAR served as an aussichtsreich prognostic indicator for neuroblastoma, and the nomogram model based on LAR can predict the OS of patients with this condition. Full article

Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults, with a median overall survival of fewer than 15 months despite standard-of-care treatment. Accurate preoperative prognostication is essential for personalized treatment planning; however, existing approaches rely primarily on magnetic resonance imaging (MRI) and often overlook the rich histopathological information contained in postoperative whole-slide images (WSIs). The inherent spatiotemporal gap between preoperative MRI and postoperative WSIs substantially hinders effective multimodal integration. To address this limitation, we propose a contrastive-learning-based Imaging–Pathology Synergistic Alignment (CL-IPSA) framework that aligns MRI and WSI data within a shared embedding space, thereby establishing robust cross-modal semantic correspondences. We further construct a cross-modal mapping library that enables patients with MRI-only data to obtain proxy pathological representations via nearest-neighbor retrieval for joint survival modeling. Experiments across multiple datasets demonstrate that incorporating proxy WSI features consistently enhances prediction performance: various convolutional neural networks (CNNs) achieve an average AUC improvement of 0.08–0.10 on the validation cohort and two independent test sets, with SEResNet34 yielding the best performance (AUC = 0.836). Our approach enables non-invasive, preoperative integration of radiological and pathological semantics, substantially improving GBM survival prediction without requiring any additional invasive procedures. Full article

Background: Immune checkpoint blockade (ICB) has revolutionized lung adenocarcinoma (LUAD) therapy, yet predictive bio-markers remain suboptimal. We hypothesized that BCL2A1 expression in CD8⁺ T cells may reflect immune endurance and complement PD-L1 in predicting ICB response. Methods: Integrating bulk and single-cell RNA-seq across multiple LUAD cohorts, this study performed differential expression, survival, and pathway analyses in a discovery cohort (n = 60) and validated findings across five independent cohorts (n = 126). Results: Single-cell profiling identified BCL2A1 enrichment in tissue-resident memory and proliferating subsets that appeared preferentially expanded in responders; cell–cell communication analysis revealed that BCL2A1^high CD8⁺ T cells exhibited significantly enhanced outgoing signaling capacity (p = 0.0278), with proliferating subsets serving as intra-CD8⁺ coordination hubs and MIF pathway interactions achieving the highest intensity among all axes examined. BCL2A1 was significantly upregulated in responders (FDR < 0.05) and associated with improved ICB survival (HR = 0.43, p < 0.05), but not in non-ICB settings, suggesting treatment-specific prognostic relevance. A tri-marker model integrating BCL2A1, PD-L1 (CD274), and a 27-gene HOT score demonstrated favorable predictive performance (AUC = 0.826 discovery; macro-AUC = 0.774 validation), outperforming PD-L1 alone (AUC = 0.706) and established signatures including TIDE, IPS, TIS, and IFNG. Cross-platform simulations suggested high reproducibility (ρ = 0.982–0.993). Conclusions: These findings suggest BCL2A1 may serve as a bio-marker of CD8⁺ T-cell survival and enhanced intercellular coordination, and its integration with PD-L1 and immune activation markers may yield a reproducible ICB response predictor, pending clinical validation. Full article

Background: The Lung Immune Prognostic Index (LIPI) has recently emerged as a novel prognostic biomarker in several malignancies, particularly in patients receiving immunotherapy. However, its role in renal cell carcinoma (RCC), especially in non-metastatic and tyrosine kinase inhibitor (TKI)-treated patients, remains unclear. Methods: In this retrospective cohort study, 153 patients diagnosed with RCC between 2012 and 2024 were analyzed. Prognostic scores including LIPI, International Metastatic RCC Database Consortium (IMDC), and Memorial Sloan Kettering Cancer Center (MSKCC) scores were calculated. The patients were stratified into risk groups (good, intermediate, and poor) based on these scores. Survival analyses were performed using Kaplan–Meier and Cox regression methods. Correlations between scoring systems were assessed using Pearson’s correlation. Results: The median follow-up was 29.1 months. A total of 55 (35.9%) patients had metastases at diagnosis. LIPI was significantly associated with overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS) (p < 0.05). In the multivariate Cox analysis, LIPI remained an independent prognostic factor for OS and PFS. Strong positive correlations were found between LIPI and both IMDC and MSKCC scores (r > 0.6, p < 0.001). Notably, LIPI demonstrated prognostic relevance even in patients treated with TKIs. Conclusions: LIPI is a simple and accessible prognostic tool that provides significant survival stratification in RCC patients. Its predictive utility extends beyond immunotherapy cohorts, indicating potential value in broader RCC management. Integration of LIPI into current prognostic models may improve individualized treatment approaches. Full article

Background: This systematic review aims to identify modifiable and non-modifiable predictors of exercise capacity (VO₂peak level or change) in stroke survivors. These insights may further optimize rehabilitation treatment and improve long-term health outcomes. Methods: PubMed (PubMed.gov), EMBASE (Elsevier), CINAHL (EBSCO), and Web of Science (Clarivate) were searched (last search on 7 October 2025). Inclusion criteria were: (1) adults (>18 years) who survived a stroke (ischemic and hemorrhagic), (2) outcome was a measurement of maximum exercise capacity (VO₂peak) measured with CPET (or equivalent), (3) predictors of exercise capacity were measured (e.g., personal factors, disease-related factors, components of rehabilitation), (4) predictors of exercise capacity were analyzed in multivariate regression models, (5) primary research, and (6) full-text available. During the data extraction phase, predictors were categorized into modifiable and non-modifiable predictors. Risk of bias was assessed with the McMaster Critical Review Form for Quantitative Studies. Results: Of 919 unique articles, seventeen were included. Modifiable factors such as BMI (4/8 articles) and fat mass (1/1), lower limb strength (3/3), cardiorespiratory fitness (e.g., baseline VO₂peak (2/4)), training intensity (2/2) and perceived fatigue (1/1) significantly predicted VO₂peak (level or change). Significant non-modifiable predictors were age (3/11), sex (1/8), diabetes (1/2), and stroke-specific (4/8) factors. Conclusions: This systematic review highlights the significant role of modifiable and non-modifiable predictors in optimizing exercise capacity (VO₂peak) for stroke survivors. In addition, considering modifiable and non-modifiable predictors allows for more personalized treatment planning. The findings can guide healthcare professionals in tailoring rehabilitation programs, though further research is needed to develop a comprehensive prediction model. Full article

Background/Objectives: Ovarian cancer has the highest mortality among gynecological malignancies, with platinum resistance significantly contributing to poor prognosis. We aimed to develop a multimodal model (MMHC-OCPR) to predict platinum response and recurrence risk, enabling earlier personalized treatment and improved outcomes. Methods: This multicenter retrospective study included a combined cohort of 431 patients, comprising 1182 whole slide images (WSIs) curated from two independent datasets. The primary cohort consisted of 376 patients from the National Cancer Center (China), which was further partitioned into training, validation and internal test sets to ensure model development and evaluation. An additional external test cohort was incorporated using publicly available data from TCGA, enhancing the generalizability of our findings. We implemented a weakly supervised multiple instance learning framework to integrate histopathological imaging with clinicopathological variables, further strengthened by the incorporation of the transformer-based pretrained encoder UNI2-h, which enhanced the model’s predictive performance. Results: All patients in the primary cohort had pathology slides collected from primary ovarian tumors and metastatic tumor, along with clinical factors related to prognosis and treatment response. The baseline platinum response classifier using primary WSIs achieved an AUC of 0.896 in the internal test group and 0.876 in the external test group. Integration of metastatic WSIs and clinical data inputs yielded a superior AUC of 0.914 in the internal test set. The recurrence risk model demonstrated a C-index of 0.801, rising to 0.838 after multimodal enhancement. The model stratified patients into low-, intermediate- and high-risk groups with 2-year progression-free survival rates of 77.3%, 48.0% and 2.0%, respectively. Conclusions: Our model enables the early detection of platinum resistance, guiding timely treatment intensification. The recurrence risk stratification supports personalized management by identifying patients with favorable outcomes following surgery and chemotherapy, potentially sparing them from maintenance therapy to reduce associated toxicity, cost, and enhance quality of life. Full article

Despite successful revascularization, patients with non-ST elevation myocardial infarction (NSTEMI) remain at higher risk of mortality and morbidity. Accurately predicting mortality risk in this cohort can improve outcomes through timely interventions. This study for the first time predicts 1-year all-cause mortality in an NSTEMI cohort using features extracted primarily from the aortic pressure (AP) signal recorded during cardiac catheterization. We analyzed data from 497 NSTEMI patients (66.3 ± 12.9 years, 187 (37.6%) females) retrospectively. We developed three survival models, the multivariate Cox proportional hazards, DeepSurv, and random survival forest, to predict mortality. Then, used Shapley additive explanations (SHAP) to interpret the decision-making process of the best survival model. Using 5-fold stratified cross-validation, DeepSurv achieved an average C-index of 0.935, an IBS of 0.028, and a mean time-dependent AUC of 0.939, outperforming the other models. Ejection systolic time, ejection systolic period, the difference between systolic blood pressure and dicrotic notch pressure (DesP), skewness, the age-modified shock index, and myocardial oxygen supply/demand ratio were identified by SHAP as the most characteristic AP features. In conclusion, AP signal features offer valuable prognostic insight for predicting 1-year all-cause mortality in the NSTEMI population, leading to enhanced risk stratification and clinical decision-making. Full article

Background: Interleukin-38 (IL-38) is an anti-inflammatory IL-1—family cytokine implicated in limiting tissue injury by its anti-inflammatory character. We evaluated the diagnostic discrimination and prognostic relevance in steatotic liver disease (SLD). Methods: We conducted a prospective, monocentric cohort analysis of 184 patients with SLD (n = 176) and healthy controls (n = 8). We tested group differences using Mann–Whitney U or Kruskal–Wallis; determined diagnostic quality using ROC curves. Logistic regression was used to assess the relationship with decompensation. Associations with MELD and routine laboratory parameters were modeled using Spearman correlation and linear regression. We analyzed survival using Kaplan–Meier and Cox regression. Findings: IL-38 concentrations were found to be higher in decompensated (n = 94) than in compensated patients (n = 82) (p < 0.001). MELD was positively associated with IL-38 (p < 0.001; 95% CI 0.057–0.120). This corresponds to a 9.2% increase in IL-38 per 1-point increase in MELD (95% CI 5.9–12.7%). IL-38 correlated positively with the MELD score (p < 0.001) and with bilirubin/AST/LDH. In the combination model (MELD + IL-38 ± CRP), a very good AUC ≈ 0.92 was achieved. Conclusion: IL-38 reflects the severity of steatotic liver disease and is therefore a potentially predictive biomarker for early risk stratification and therapy monitoring. Full article

Background and Objectives: Thoracic trauma usually results in high morbidity and mortality. It is the leading cause of death in patients within the first four decades of life. In this study, we aimed to identify risk factors for intensive care mortality and to evaluate factors affecting clinical outcomes and complications in patients with thoracic trauma who were treated in the intensive care unit (ICU). Materials and Methods: This was a retrospective, single-center study. Patients diagnosed with thoracic trauma and followed up in the ICU between 1 May 2023 and 1 January 2025 were included. Critically ill patients aged 18 years and older whose admission blood values were available and who had undergone radiological imaging were included in the study. Patients were grouped as Survivors or Non-survivors. The primary outcome was to determine risk factors for mortality. The secondary outcome was to evaluate factors affecting clinical outcomes and complications. The tertiary outcome was to determine the predictive value of the Injury Severity Score (ISS), Acute Physiology and Chronic Health Evaluation II (APACHE II), and Glasgow Coma Scale (GCS) for mortality. Results: A total of 104 patients (male/female ratio: 76/28) were included in the study. Twenty-four patients (23.1%) died, and eighty (76.9%) were discharged. Age in the Non-survivor group was found to be significantly higher (59.33 ± 22.21 vs. 40.50 ± 17.71; p < 0.001), and the proportion of women was also significantly higher in the Non-survivor group (p = 0.0082). Mortality was associated with advanced age, female sex, lower GCS score (p < 0.001), higher APACHE II scores (p < 0.001), and the presence of comorbid conditions (p = 0.003), including head trauma (p = 0.024) and cardiac arrest before ICU admission (p = 0.011). The Non-survivor group more frequently required mechanical ventilation (p < 0.001), vasopressor support (p < 0.001), and continuous renal replacement therapy (p < 0.001), and they developed ventilator-associated pneumonia (p < 0.001) and acute respiratory distress syndrome (p < 0.001) at higher rates. ICU length of stay was also significantly longer in the Non-survivor group (p = 0.045). The APACHE II score demonstrated the highest discriminatory performance, emerging as the strongest clinical predictor of mortality (AUC = 0.751, 95% CI: 0.630–0.872; p < 0.001). Age (OR: 1.06) and serum lactate levels (OR: 1.57) consistently emerged as strong independent predictors of mortality. The presence of head trauma significantly increased the risk of mortality, particularly in the APACHE II-adjusted model (OR: 9.08). The APACHE II–based model yielded high specificity (96.3%) and accuracy (88.5%), with good discrimination (AUC = 0.894) and the highest Nagelkerke R² (0.548). Conclusions: Factors that may shorten the length of ICU stay include infection control, early correction of acidosis, and maintenance of hemodynamic stability, which may reduce mortality. APACHE II was more closely related to overall clinical severity than the other scoring systems. Our data indicate that age-related frailty and acute physiological derangement, as best represented by the APACHE II score, are more significant determinants of survival than anatomic injury severity alone. Full article