Search Results (9,799)

The adequate survival of probiotics in the harsh environment of the gastrointestinal (GI) tract plays a crucial role in the expression of their functional properties. The aim of the present study was to evaluate the survival of commercial probiotics during digestion using a standardised INFOGEST 2.0 model extended with three food matrices simulating three scenarios for the consumption of probiotics: on an empty stomach, with juice, or with food (porridge). All eight products matched the bacterial content stated on the label. After simulated digestion, we observed an average decrease in viability of 1.6 log10 colony forming units (CFU) when the product was co-digested with water, a 2.5 log10 CFU decrease in the presence of juice, and a 1.2 log10 CFU decrease in the presence of porridge. The survival rate of the probiotics was statistically higher in the test samples with porridge (91.8%) than in those with juice (79.0%). For two products, the number of lactobacilli and bifidobacteria strains after digestion was less than <3 × 10⁵ CFU, which can be considered insufficient. The present study has shown that the survival of probiotic strains during GI passage depends not only on their ability to withstand these harsh conditions but may also be influenced by the manufacturing process and by the foods consumed together with the probiotics. Full article

We evaluated the efficacy of the adoptive transfer of memory B, CD4+, and CD8+ T lymphocytes compared with sulbactam and tigecycline in an experimental murine pneumonia model by two multidrug-resistant Acinetobacter baumannii strains, colistin-susceptible AbCS01 and colistin-resistant AbCR17. Pharmacodynamically optimized antimicrobial dosages were administered for 72 h, and intravenous administration of 2 × 10⁶ of each of the memory cells in a single dose 30 min post-infection. Bacterial lung and blood counts and mortality rates were analyzed. Results showed that a single dose of memory B or CD4+ T cells was as effective as sulbactam in terms of bacterial clearance from the lungs and blood compared with the untreated mice or the tigecycline-treated mice inoculated with the AbCS01 strain. In the pneumonia model by AbCR17, a single dose of memory B or CD4+ T cells also reduced the bacterial load in the lungs compared with both antibiotic groups and was more efficacious than tigecycline in terms of blood clearance. Regarding survival, the adoptive transfer of memory B or CD4+ T cells was as effective as three days of sulbactam treatment for both strains. These data suggest that adoptive memory cell transfer could be a new effective treatment of multidrug-resistant A. baumannii infections. Full article

This study presents an innovative method for producing thermosensitive bioink from chitosan hydrogels saturated with carbon dioxide and agarose. It focuses on a detailed characterisation of their physicochemical properties and potential applications in biomedicine and tissue engineering. The ORO test approved the rapid regeneration of the three-dimensional structure of chitosan–agarose composites in a unidirectional bench press simulation test. The diffusion of dyes through the chitosan–agarose hydrogel membranes strongly depended on the share of both polymers in the composite and the molecular weight of the dyes. Glucose, as a nutrient marker, also diffused through all membranes regardless of composition. Biocompatibility assessment using MTT tests on 46BR.1N fibroblasts and HaCaT keratinocytes confirmed the safety of the bioink. The regenerative potential of the bioink was confirmed by efficient cell migration, especially HaCaT. Long-term viability studies showed that chitosan–agarose scaffolds, unlike the agarose ones, support cell proliferation and survival, especially 14 days after bioink extrusion. Experiments in a skin wound model in mice confirmed the biocompatibility of the tested dressing and the beneficial action of chitosan on healing. Studies on vessel formation in chicken embryos highlight the potential of the chitosan–agarose composition to enhance proangiogenic effects. This composition meets all entry criteria and possesses excellent biological properties. Full article

Background/Objectives: Cancer-associated cachexia in head and neck squamous cell carcinoma (HNSCC) is challenging to diagnose due to its complex pathophysiology. This study aimed to identify metabolic biomarkers linked to cachexia and survival in HNSCC patients using [¹⁸F]FDG-PET/CT imaging and machine learning (ML) techniques. Methods: We retrospectively analyzed 253 HNSCC patients from Vienna General Hospital and the MD Anderson Cancer Center. Automated organ segmentation was employed to quantify metabolic and volumetric data from [¹⁸F]FDG-PET/CT scans across 29 tissues and organs. Patients were categorized into low weight loss (LoWL; grades 0–2) and high weight loss (HiWL; grades 3–4) groups, according to the weight loss grading system (WLGS). Machine learning models, combined with Cox regression, were used to identify survival predictors. Shapley additive explanation (SHAP) analysis was conducted to determine the significance of individual features. Results: The HiWL group exhibited increased glucose metabolism in skeletal muscle and adipose tissue (p = 0.01), while the LoWL group showed higher lung metabolism. The one-year survival rate was 84.1% in the LoWL group compared to 69.2% in the HiWL group (p < 0.01). Pancreatic volume emerged as a key biomarker associated with cachexia, with the ML model achieving an AUC of 0.79 (95% CI: 0.77–0.80) and an accuracy of 0.82 (95% CI: 0.81–0.83). Multivariate Cox regression confirmed pancreatic volume as an independent prognostic factor (HR: 0.66, 95% CI: 0.46–0.95; p < 0.05). Conclusions: The integration of metabolic and volumetric data provided a strong predictive model, highlighting pancreatic volume as a key imaging biomarker in the metabolic assessment of cachexia in HNSCC. This finding enhances our understanding and may improve prognostic evaluations and therapeutic strategies. Full article

Purpose: To develop and validate an MRI-based radiomic model for predicting overall survival (OS) in patients diagnosed with glioblastoma multiforme (GBM), utilizing a retrospective dataset from multiple institutions. Materials and Methods: Pre-treatment MRI images of 289 GBM patients were collected. From each patient’s tumor volume, 660 radiomic features (RFs) were extracted and subjected to robustness analysis. The initial prognostic model with minimum RFs was subsequently enhanced by including clinical variables. The final clinical–radiomic model was derived through repeated three-fold cross-validation on the training dataset. Performance evaluation included assessment of concordance index (C-Index), integrated area under curve (iAUC) alongside patient stratification into low and high-risk groups for overall survival (OS). Results: The final prognostic model, which has the highest level of interpretability, utilized primary gross tumor volume (GTV) and one MRI modality (T2-FLAIR) as a predictor and integrated the age variable with two independent, robust RFs, achieving moderately good discriminatory performance (C-Index [95% confidence interval]: 0.69 [0.62–0.75]) with significant patient stratification (p = 7 × 10⁻⁵) on the validation cohort. Furthermore, the trained model exhibited the highest iAUC at 11 months (0.81) in the literature. Conclusion: We identified and validated a clinical–radiomic model for stratification of patients into low and high-risk groups based on OS in patients with GBM using a multicenter retrospective dataset. Future work will focus on the use of deep learning-based features, with recently standardized convolutional filters on OS tasks. Full article

Since genetic mutations during brain development play a significant role in the genesis of epilepsy, and such genetically determined epilepsies are the most difficult to treat, there is a need to study the mechanisms of epilepsy development with deletions of various transcription factors. We utilized heterozygous mice (Sip1^wt/fl) with a neuronal deletion of the transcription factor Sip1 (Smad interacting protein 1) in the cerebral cortex. These mice are characterized by cognitive impairment and are prone to epilepsy. It is known that the brain-derived neurotrophic factor (BDNF) has a neuroprotective effect in various neurodegenerative diseases. Therefore, we created and applied an adeno-associated construct carrying the BDNF sequence selectively in neurons. Using in vitro and in vivo research models, we were able to identify a key gen, the disruption of whose expression accompanies the deletion of Sip1 and contributes to hyperexcitation of neurons in the cerebral cortex. Overexpression of BDNF in cortical neurons eliminated epileptiform activity in neurons obtained from heterozygous Sip1 mice in a magnesium-free model of epileptiform activity (in vitro). Using PCR analysis, it was possible to identify correlations in the expression profile of genes encoding key proteins responsible for neurotransmission and neuronal survival. The effects of BDNF overexpression on the expression profiles of these genes were also revealed. Using BDNF overexpression in cortical neurons of heterozygous Sip1 mice, it was possible to achieve 100% survival in the pilocarpine model of epilepsy. At the level of gene expression in the cerebral cortex, patterns were established that may be involved in the protection of brain cells from epileptic seizures and the restoration of cognitive functions in mice with Sip1 deletion. Full article

Background/Objectives: Metastatic posterior uveal melanoma (PUM) is one of the deadliest types of melanomas. Though the median survival is short, some patients with metastatic disease live for a long time. In this study, we investigated whether the anatomical location of the metastatic lesions is associated with differences in survival. Methods: One hundred and seventy-eight patients with metastatic PUM with baseline whole-body imaging were retrospectively included. The patients were divided into three groups based on the anatomical location of metastases: (1) exclusive liver metastases (hepatic pattern), (2) both hepatic and extrahepatic metastatic lesions (hepatic–extrahepatic pattern), and (3) exclusive extrahepatic lesions (extrahepatic pattern). Survival was investigated using Kaplan–Meier plots, log-rank test, and the Cox proportional hazard model. Results: In total, 95 patients (53%) presented with hepatic pattern, 66 patients (37%) presented with hepatic–extrahepatic pattern, and 17 patients (10%) presented with extrahepatic pattern. Overall survival was significantly longer in patients with extrahepatic pattern (median 17.0 months) compared to those with hepatic pattern (median 11.0 months) and hepatic–extrahepatic pattern (median 7.0 months) (p < 0.001, log-rank test). Multivariate Cox regression analysis showed increased hazard ratios (HR) for hepatic pattern (HR 2.37, 95% CI 1.08–5.17, p = 0.031) and hepatic–extrahepatic pattern (3.25, 95% CI 1.42–7.41, p = 0.005) compared to extrahepatic pattern. Most patients with hepatic (95%) and hepatic–extrahepatic patterns (82%) were diagnosed with metastases by liver ultrasonography screening, whereas 81% of patients with extrahepatic pattern developed symptoms that led to the diagnosis. Conclusions: Extrahepatic pattern was associated with prolonged survival in patients with metastatic PUM, despite there being a larger proportion of symptomatic patients. It is therefore important to consider the anatomical location of the metastatic lesions when stratifying patients into clinical trials. Full article

Degeneration and death of dopaminergic neurons in the substantia nigra of the midbrain are the main pathological changes in Parkinson’s disease (PD); however, the mechanism underlying the selective vulnerability of specific neuronal populations in PD remains unclear. Here, we used single-cell RNA sequencing to identify seven cell clusters, including oligodendrocytes, neurons, astrocytes, oligodendrocyte progenitor cells, microglia, synapse-rich cells (SRCs), and endothelial cells, in the substantia nigra of a rotenone-induced rat model of PD based on marker genes and functional definitions. We found that SRCs were a previously unidentified cell subtype, and the tight interactions between SRCs and other cell populations can be improved by icaritin, which is a flavonoid extracted from Epimedium sagittatum Maxim. and exerts anti-neuroinflammatory, antioxidant, and immune-improving effects in PD. We also demonstrated that icaritin bound with transcription factors of SRCs, and icaritin application modulated synaptic characterization of SRCs, neuroinflammation, oxidative stress, and survival of dopaminergic neurons, and improved abnormal energy metabolism, amino acid metabolism, and phospholipase D metabolism of astrocytes in the substantia nigra of rats with PD. Moreover, icaritin supplementation also promotes the recovery of the physiological homeostasis of the other cell clusters to delay the pathogenesis of PD. These data uncovered previously unknown cellular diversity in a rat model of Parkinson’s disease and provide insights into the promising therapeutic potential of icaritin in PD. Full article

Background: Cancer survival prediction is vital in improving patients’ prospects and recommending therapies. Understanding the molecular behavior of cancer can be enhanced through the integration of multi-omics data, including mRNA, miRNA, and DNA methylation data. In light of these multi-omics data, we proposed a graph attention network (GAT) model in this study to predict the survival of non-small cell lung cancer (NSCLC). Methods: The different omics data were obtained from The Cancer Genome Atlas (TCGA) and preprocessed and combined into a single dataset using the sample ID. We used the chi-square test to select the most significant features to be used in our model. We used the synthetic minority oversampling technique (SMOTE) to balance the dataset and the concordance index (C-index) to measure the performance of our model on different combinations of omics data. Results: Our model demonstrated superior performance, with the highest value of the C-index obtained when we used both mRNA and miRNA data. This demonstrates that the multi-omics approach could be effective in predicting survival. Further pathway analysis conducted with KEGG showed that our GAT model provided high weights to the features that are associated with the viral entry pathways, such as the Epstein–Barr virus and Influenza A pathways, which are involved in lung cancer development. From our findings, it can be observed that the proposed GAT model leads to a significantly improved prediction of survival by exploiting the strengths of multiple omics datasets and the findings from the enriched pathways. Our GAT model outperforms other state-of-the-art methods that are used for NSCLC prediction. Conclusions: In this study, we developed a new model for the survival prediction of NSCLC using the GAT based on multi-omics data. Our model showed outstanding predictive values, and the KEGG analysis of the selected significant features showed that they were implicated in pivotal biological processes underlying pathways such as Influenza A and the Epstein–Barr virus infection, which are linked to lung cancer progression. Full article

Background: Continuous breakthroughs in computational algorithms have positioned AI-based models as some of the most sophisticated technologies in the healthcare system. AI shows dynamic contributions in advancing various medical fields involving data interpretation and monitoring, imaging screening and diagnosis, and treatment response and survival prediction. Despite advances in clinical oncology, more effort must be employed to tailor therapeutic plans based on each patient’s unique transcriptomic profile within the precision/personalized oncology frame. Furthermore, the standard analysis method is not compatible with the comprehensive deciphering of significant data streams, thus precluding the prediction of accurate treatment options. Methodology: We proposed a novel approach that includes obtaining different tumour tissues and preparing RNA samples for comprehensive transcriptomic interpretation using specifically trained, programmed, and optimized AI-based models for extracting large data volumes, refining, and analyzing them. Next, the transcriptomic results will be scanned against an expansive drug library to predict the response of each target to the tested drugs. The obtained target-drug combination/s will be then validated using in vitro and in vivo experimental models. Finally, the best treatment combination option/s will be introduced to the patient. We also provided a comprehensive review discussing AI models’ recent innovations and implementations to aid in molecular diagnosis and treatment planning. Results: The expected transcriptomic analysis generated by the AI-based algorithms will provide an inclusive genomic profile for each patient, containing statistical and bioinformatics analyses, identification of the dysregulated pathways, detection of the targeted genes, and recognition of molecular biomarkers. Subjecting these results to the prediction and pairing AI-based processes will result in statistical graphs presenting each target’s likely response rate to various treatment options. Different in vitro and in vivo investigations will further validate the selection of the target drug/s pairs. Conclusions: Leveraging AI models will provide more rigorous manipulation of large-scale datasets on specific cancer care paths. Such a strategy would shape treatment according to each patient’s demand, thus fortifying the avenue of personalized/precision medicine. Undoubtedly, this will assist in improving the oncology domain and alleviate the burden of clinicians in the coming decade. Full article

Background: Gliomas, the most prevalent type of primary brain tumor, stand out as one of the most aggressive and lethal types of human cancer. Methods & Results: To uncover potential prognostic markers, we employed the weighted correlation network analysis (WGCNA) on the Chinese Glioma Genome Atlas (CGGA) 693 dataset to reveal four modules significantly associated with glioma clinical traits, primarily involved in immune function, cell cycle regulation, and ribosome biogenesis. Using the least absolute shrinkage and selection operator (LASSO) regression algorithm, we identified 11 key genes and developed a prognostic risk score model, which exhibits precise prognostic prediction in the CGGA 325 dataset. More importantly, we also validated the model in 12 glioma patients with overall survival (OS) ranging from 4 to 132 months using mRNA sequencing and immunohistochemical analysis. The analysis of immune infiltration revealed that patients with high-risk scores exhibit a heightened immune infiltration, particularly immune suppression cells, along with increased expression of immune checkpoints. Furthermore, we explored potentially effective drugs targeting 11 key genes for gliomas using the library of integrated network-based cellular signatures (LINCS) L1000 database, identifying that in vitro, both torin-1 and clofarabine exhibit promising anti-glioma activity and inhibitory effect on the cell cycle, a significant pathway enriched in the identified glioma modules. Conclusions: In conclusion, our study provides valuable insights into molecular mechanisms and identifying potential therapeutic targets for gliomas. Full article

Background: Busulfan is an FDA-approved alkylating drug used in the chemotherapy of advanced acute myeloid leukemia. The precise mechanisms by which Busulfan kills spermatogonia stem cells (SSCs) are not yet completely understood. Methods: Using a murine model, we evaluated Busulfan-induced apoptosis and DNA damage signaling between testis and ovary tissues. We executed RT-qPCR, analyzed single-nuclei RNA sequencing data and performed in situ hybridization for the localization of the gene expression in the tissues. Results: The results indicate that, in contrast to female germ cells, haploid male germ cells undergo significant apoptosis following Busulfan chemotherapy. Moreover, a gene enrichment analysis revealed that reactive oxygen species may activate the inflammatory response in part through the TNF-α/NF-κB signaling pathway. Interestingly, in the testis, the mRNA levels of TNF-α and TAF7 (TATA box-binding protein-associated factor 7) are downregulated, and testosterone levels suppressed. Mechanistically, the promoter of TNF-α has a conserved motif for binding TAF7, which is necessary for its transcriptional activation and may require further in-depth study. We next analyzed the tumorigenic function of TAF7 and revealed that it is highly overexpressed in several types of human cancers, particularly testicular germ cell tumors, and associated with poor patient survival. Therefore, we executed in situ hybridization and single-nuclei RNA sequencing, finding that less TAF7 mRNA is present in SSCs after chemotherapy. Conclusions: Thus, our data indicate a possible function of TAF7 in the regulation of SSCs and spermatogenesis following downregulation by Busulfan. These findings may account for the therapeutic effects of Busulfan and underlie its potential impact on cancer chemotherapy prognosis. Full article

Introduction: The management of anemia in chronic kidney disease (CKD-An) presents significant challenges for nephrologists due to variable responsiveness to erythropoietin-stimulating agents (ESAs), hemoglobin (Hb) cycling, and multiple clinical factors affecting erythropoiesis. The Anemia Control Model (ACM) is a decision support system designed to personalize anemia treatment, which has shown improvements in achieving Hb targets, reducing ESA doses, and maintaining Hb stability. This study aimed to evaluate the association between ACM-guided anemia management with hospitalizations and survival in a large cohort of hemodialysis patients. Methods: This multi-center, retrospective cohort study evaluated adult hemodialysis patients within the European Fresenius Medical Care NephroCare network from 2014 to 2019. Patients treated according to ACM recommendations were compared to those from centers without ACM. Data on demographics, comorbidities, and dialysis treatment were used to compute a propensity score estimating the likelihood of receiving ACM-guided care. The primary endpoint was hospitalizations during follow-up; the secondary endpoint was survival. A 1:1 propensity score-matched design was used to minimize confounding bias. Results: A total of 20,209 eligible patients were considered (reference group: 17,101; ACM adherent group: 3108). Before matching, the mean age was 65.3 ± 14.5 years, with 59.2% men. Propensity score matching resulted in two groups of 1950 patients each. Matched ACM adherent and non-ACM patients showed negligible differences in baseline characteristics. Hospitalization rates were lower in the ACM group both before matching (71.3 vs. 82.6 per 100 person-years, p < 0.001) and after matching (74.3 vs. 86.7 per 100 person-years, p < 0.001). During follow-up, 385 patients died, showing no significant survival benefit for ACM-guided care (hazard ratio = 0.93; p = 0.51). Conclusions: ACM-guided anemia management was associated with a significant reduction in hospitalization risk among hemodialysis patients. These results further support the utility of ACM as a decision-support tool enhancing anemia management in clinical practice. Full article

Avoiding downtime is one of the major concerns of manufacturing industries. In the era of connected industry, acquiring data has become cheaper than ever; however, turning that data into actionable insights for operators is not always straightforward. In this work, we present a manufacturing scenario involving a circular blade rubber cutting machine, where the goal is to minimize downtime. Historical cutting data are available, and the aim is to provide the machine operators with an intuitive tool that helps them reduce this downtime. This work demonstrates how, in an Industry 4.0 environment, data can be leveraged to minimize downtime. To achieve this, different survival model approaches are compared, a Health Index (HI) is developed, and the model deployment is analysed, highlighting the importance of understanding the model as a dynamic system in which the operator plays a key role. Full article

Cord-rubber composites are subjected to a wide range of loads in various applications. However, their fatigue behavior remains relatively under-researched. To address this gap, a set of representative specimens was developed, and a validated numerical model was employed to assess fatigue-relevant parameters. In this study, we present the results from two series of tests with different strain ratios (R values). One series was subjected to a pure pulsating tensile strain (R ~0), while the second series experienced an increased mean strain with an R ratio between 0.2 and 0.3. A direct comparison of the two series demonstrated that a higher strain ratio results in a longer service life. This is reflected in an increase in the slope (k) from 13 to 23, as well as an increase in the ultimate fiber strain from 8% to 11% at N_d = 50,000 load cycles for a survival probability of 50%. Both series indicate a comparable scatter in the test results. This comparative analysis shows that the strain ratio significantly impacts the fatigue behavior of cord-rubber composite materials based on cyclic tests under different loading conditions. The findings of this study demonstrate the necessity of considering different load situations when evaluating or designing components. Full article