Search Results (3,783)

Background: Septic arthritis is an orthopedic emergency. However, optimal biomarkers and diagnostic criteria remain unclear. The study aimed to evaluate the diagnostic performance of routinely used and novel biomarkers, including serum C-reactive protein (CRP), synovial white blood cells (WBC), pentraxin-3 (PTX3), interleukin-6 (IL-6), and presepsin, in distinguishing septic from non-septic arthritis. Methods: Thirty-one patients undergoing arthrocentesis were included. Patients were categorized into septic and non-septic arthritis groups. Synovial fluid and serum samples were analyzed for five biomarkers. Diagnostic performance was assessed by calculating the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: Synovial WBC demonstrated the highest diagnostic performance among single biomarkers (AUC = 0.837, p = 0.012). Among novel biomarkers, PTX3 showed the highest accuracy and sensitivity. The serum CRP and synovial WBC combination yielded an AUC of 0.853, with 100% sensitivity, 68.0% specificity, 42.9% PPV, and 100% NPV. Adding all three novel biomarkers to this combination increased the AUC to 0.887 (p = 0.004), maintaining 100% sensitivity and NPV. When individually added, PTX3 achieved 100% sensitivity and NPV, while presepsin showed the highest specificity (96.0%), PPV (75.0%), and accuracy (87.1%). Conclusions: Serum CRP and synovial WBC remain essential biomarkers for diagnosing septic arthritis; however, combining them with PTX3, IL-6, and presepsin improved diagnostic accuracy. PTX3 is best suited for ruling out septic arthritis due to its high sensitivity and NPV, whereas presepsin is more useful for confirmation, given its specificity and PPV. These results support a tailored biomarker approach aligned with diagnostic intent. Full article

Sustainability represents a significant global challenge, requiring a balance between environmental impact and the use of natural resources. White biotechnology, which uses microorganisms and enzymes for environmentally friendly products and processes, offers promising solutions to support a growing population. Within this context, the yeast Yarrowia lipolytica stands out, so we investigated the generation of biomass from two wild strains (ATCC 9773 and NRRL Y-50997) using different carbon sources. Additionally, protein content and amino acid profiles were assessed via standardized analytical methods to evaluate their potential as nutritional yeasts. Both strains demonstrated potential as nutritional yeasts, with biomass productivities of up to 35.5 g/L and 42 g/L, respectively. The protein content was high, with 58.8% for ATCC 9773 and 58.2% for NRRL Y-50997. Furthermore, the strains presented essential amino acid contents of 62.6% and 41.5%, with lysine being the most abundant amino acid. These findings underscore the versatility and productivity of Y. lipolytica, highlighting its potential for sustainable biotechnological applications such as single-cell protein production. Full article

Triple-negative breast cancer (TNBC) is a clinically and molecularly heterogeneous subtype defined by the absence of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression. In this study, tumor specimens from 104 TNBC patients were analyzed to characterize molecular and clinicopathological features and to assess the expression and therapeutic potential of four key surface markers: epidermal growth factor receptor (EGFR), epithelial cell adhesion molecule (EpCAM), tissue factor (TF), and trophoblast cell surface antigen (TROP2). Multiplex immunofluorescence (mIF) demonstrated elevated EGFR and TROP2 expression in the majority of samples. Significant positive correlations were observed between EGFR and TF, as well as between TROP2 and both TF and EpCAM. Expression analyses revealed increased EGFR and TF levels with advancing tumor stage, whereas EpCAM expression declined in advanced-stage tumors. TROP2 and TF expression were significantly elevated in higher-grade tumors. Additionally, EGFR and EpCAM levels were significantly higher in patients with elevated Ki-67 indices. Binding specificity assays using single-chain variable fragment (scFv-SNAP) fusion proteins confirmed robust targeting efficacy, particularly for EGFR and TROP2. These findings underscore the therapeutic relevance of EGFR and TROP2 as potential biomarkers and targets in TNBC. Full article

The use of methane as a carbon source for producing bacterial single-cell protein (SCP) has been one of the most interesting developments in recent years. Most of these upcoming industries are using a methanotroph, Methylococcus capsulatus Bath, for SCP production using natural gas as the substrate. In the present study, we have explored the possibility of using an indigenously isolated methanotroph from a rice field in India, Methylomagnum ishizawai strain KRF4, for producing SCP from biogas [derived from cow dung]. The process was eco-friendly, required minimal instruments and chemicals, and was carried out under semi-sterile conditions in a tabletop fish tank. As the name suggests, Methylomagnum is a genus of large methanotrophs, and the strain KRF4 had elliptical to rectangular size and dimensions of ~4–5 µm × 1–2 µm. In static cultures, when biogas and air were supplied in the upper part of the growing tank, the culture grew as a thick pellicle/biofilm that could be easily scooped. The grown culture was mostly pure, from the microscopic observations where the large size of the cells, with rectangular-shaped cells and dark granules, could easily help identify any smaller contaminants. Additionally, the large cell size could be advantageous for separating biomass during downstream processing. The amino acid composition of the lyophilized biomass was analyzed using HPLC, and it was seen that the amino acid composition was comparable to commercial fish meal, soymeal, Pruteen, and the methanotroph-derived SCP-UniProtein^®. The only difference was that a slightly lower percentage of lysine, tryptophan, and methionine was observed in Methylomagnum-derived SCP. Methylomagnum ishizawai could be looked at as an alternative for SCP derived from methane or biogas due to the comparable SCP produced, on the qualitative level. Further intensive research is needed to develop a continuous, sustainable, and economical process to maximize biomass production and downstream processing. Full article

Glioblastoma (GBM) is a highly aggressive and heterogeneous brain tumor. Glioma stem-like cells (GSCs) play a central role in tumor progression, therapeutic resistance, and recurrence. Although immune cells are known to shape the GBM microenvironment, the impact of antigen-presenting-cell (APC) signature genes on tumor-intrinsic phenotypes remains underexplored. We analyzed both bulk- and single-cell RNA sequencing datasets of GBM to investigate the association between APC gene expression and tumor-cell states, including stemness and metabolic reprogramming. Signature scores were computed using curated gene sets related to APC activity, KEGG metabolic pathways, and cancer hallmark pathways. Protein–protein interaction (PPI) networks were constructed to examine the links between immune regulators and metabolic programs. The high expression of APC-related genes, such as HLA-DRA, CD74, CD80, CD86, and CIITA, was associated with lower stemness signatures and enhanced inflammatory signaling. These APC-high states (mean difference = –0.43, adjusted p < 0.001) also showed a shift in metabolic activity, with decreased oxidative phosphorylation and increased lipid and steroid metabolism. This pattern suggests coordinated changes in immune activity and metabolic status. Furthermore, TNF-α and other inflammatory markers were more highly expressed in the less stem-like tumor cells, indicating a possible role of inflammation in promoting differentiation. Our findings revealed that elevated APC gene signatures are associated with more differentiated and metabolically specialized GBM cell states. These transcriptional features may also reflect greater immunogenicity and inflammation sensitivity. The APC metabolic signature may serve as a useful biomarker to identify GBM subpopulations with reduced stemness and increased immune engagement, offering potential therapeutic implications. Full article

Background: Oxidative stress is a critical factor contributing to male infertility, impairing spermatogonial stem cells (SSCs) and disrupting normal spermatogenesis. This study aimed to isolate and characterize human SSCs and to investigate oxidative stress-related gene expression, protein interaction networks, and developmental trajectories involved in SSC function. Methods: SSCs were enriched from human orchiectomy samples using CD49f-based magnetic-activated cell sorting (MACS) and laminin-binding matrix selection. Enriched cultures were assessed through morphological criteria and immunocytochemistry using VASA and SSEA4. Transcriptomic profiling was performed using microarray and single-cell RNA sequencing (scRNA-seq) to identify oxidative stress-related genes. Bioinformatic analyses included STRING-based protein–protein interaction (PPI) networks, FunRich enrichment, weighted gene co-expression network analysis (WGCNA), and predictive modeling using machine learning algorithms. Results: The enriched SSC populations displayed characteristic morphology, positive germline marker expression, and minimal fibroblast contamination. Microarray analysis revealed six significantly upregulated oxidative stress-related genes in SSCs—including CYB5R3 and NDUFA10—and three downregulated genes, such as TXN and SQLE, compared to fibroblasts. PPI and functional enrichment analyses highlighted tightly clustered gene networks involved in mitochondrial function, redox balance, and spermatogenesis. scRNA-seq data further confirmed stage-specific expression of antioxidant genes during spermatogenic differentiation, particularly in late germ cell stages. Among the machine learning models tested, logistic regression demonstrated the highest predictive accuracy for antioxidant gene expression, with an area under the curve (AUC) of 0.741. Protein oxidation was implicated as a major mechanism of oxidative damage, affecting sperm motility, metabolism, and acrosome integrity. Conclusion: This study identifies key oxidative stress-related genes and pathways in human SSCs that may regulate spermatogenesis and impact sperm function. These findings offer potential targets for future functional validation and therapeutic interventions, including antioxidant-based strategies to improve male fertility outcomes. Full article

Pancreatic cancer is frequently accompanied by cancer-associated cachexia, a debilitating metabolic syndrome marked by progressive skeletal muscle wasting and systemic metabolic dysfunction. This study presents a systems biology framework to simultaneously identify therapeutic targets for both pancreatic ductal adenocarcinoma (PDAC) and its associated cachexia (PDAC-CX), using cell-specific genome-scale metabolic models (GSMMs). The human metabolic network Recon3D was extended to include protein synthesis, degradation, and recycling pathways for key inflammatory and structural proteins. These enhancements enabled the reconstruction of cell-specific GSMMs for PDAC and PDAC-CX, and their respective healthy counterparts, based on transcriptomic datasets. Medium-independent metabolic biomarkers were identified through Parsimonious Metabolite Flow Variability Analysis and differential expression analysis across five nutritional conditions. A fuzzy multi-objective optimization framework was employed within the anticancer target discovery platform to evaluate cell viability and metabolic deviation as dual criteria for assessing therapeutic efficacy and potential side effects. While single-enzyme targets were found to be context-specific and medium-dependent, eight combinatorial targets demonstrated robust, medium-independent effects in both PDAC and PDAC-CX cells. These include the knockout of SLC29A2, SGMS1, CRLS1, and the RNF20–RNF40 complex, alongside upregulation of CERK and PIKFYVE. The proposed integrative strategy offers novel therapeutic avenues that address both tumor progression and cancer-associated cachexia, with improved specificity and reduced off-target effects, thereby contributing to translational oncology. Full article

Background and objectives: Adducins are cytoskeletal proteins essential for membrane stability, actin–spectrin network organization, and cell signaling. Mutations in the genes ADD1, ADD2, and ADD3 have been linked to hypertension, neurodevelopmental disorders, and cancer. However, no comprehensive in silico saturation mutagenesis study has systematically evaluated the pathogenic potential and structural consequences of all possible missense mutations in adducins. This study aimed to identify high-risk variants and their potential impact on protein stability and function. Methods: We performed computational saturation mutagenesis for all possible single amino acid substitutions across the adducin proteins family. Pathogenicity predictions were conducted using four independent tools: AlphaMissense, Rhapsody, PolyPhen-2, and PMut. Predictions were validated against UniProt-annotated pathogenic variants. Predictive performance was assessed using Cohen’s Kappa, sensitivity, and precision. Mutations with a prediction probability ≥ 0.8 were further analyzed for structural stability using mCSM, DynaMut2, MutPred2, and Missense3D, with particular focus on functionally relevant domains such as phosphorylation and calmodulin-binding sites. Results: PMut identified the highest number of pathogenic mutations, while PolyPhen-2 yielded more conservative predictions. Several high-risk mutations clustered in known regulatory and binding regions. Substitutions involving glycine were consistently among the most destabilizing due to increased backbone flexibility. Validated variants showed strong agreement across multiple tools, supporting the robustness of the analysis. Conclusions: This study highlights the utility of multi-tool bioinformatic strategies for comprehensive mutation profiling. The results provide a prioritized list of high-impact adducin variants for future experimental validation and offer insights into potential therapeutic targets for disorders involving ADD1, ADD2, and ADD3 mutations. Full article

Prognostic markers such as overall survival (OS) and tertiary lymphoid structure (TLS) ratios, alongside diagnostic signatures like primary cancer-type classification, provide critical information for treatment selection, risk stratification, and longitudinal care planning across the oncology continuum. However, extracting these signals solely from sparse, high-dimensional multi-omics data remains a major challenge due to heterogeneity and frequent missingness in patient profiles. To address this challenge, we present SeNMo, a self-normalizing deep neural network trained on five heterogeneous omics layers—gene expression, DNA methylation, miRNA abundance, somatic mutations, and protein expression—along with the clinical variables, that learns a unified representation robust to missing modalities. Trained on more than 10,000 patient profiles across 32 tumor types from The Cancer Genome Atlas (TCGA), SeNMo provides a baseline that can be readily fine-tuned for diverse downstream tasks. On a held-out TCGA test set, the model achieved a concordance index of 0.758 for OS prediction, while external evaluation yielded 0.73 on the CPTAC lung squamous cell carcinoma cohort and 0.66 on an independent 108-patient Moffitt Cancer Center cohort. Furthermore, on Moffitt’s cohort, baseline SeNMo fine-tuned for TLS ratio prediction aligned with expert annotations (p < 0.05) and sharply separated high- versus low-TLS groups, reflecting distinct survival outcomes. Without altering the backbone, a single linear head classified primary cancer type with 99.8% accuracy across the 33 classes. By unifying diagnostic and prognostic predictions in a modality-robust architecture, SeNMo demonstrated strong performance across multiple clinically relevant tasks, including survival estimation, cancer classification, and TLS ratio prediction, highlighting its translational potential for multi-omics oncology applications. Full article

Background/Objectives: Magnetic iron oxide nanoparticles (IONPs), human serum albumin (HSA) and folic acid (FA) are prospective components for hybrid nanosystems for various biomedical applications. The magnetic nanosystems FA-HSA@IONPs (FAMs) containing IONPs, HSA, and FA residue are engineered in the study. Methods: Composition, stability and integrity of the coating, and peroxidase-like activity of FAMs are characterized using UV/Vis spectrophotometry (colorimetric test using o-phenylenediamine (OPD), Bradford protein assay, etc.), spectrofluorimetry, dynamic light scattering (DLS) and electron magnetic resonance (EMR). The selectivity of the FAMs accumulation in cancer cells is analyzed using flow cytometry and confocal laser scanning microscopy. Results: FAMs (d_N~55 nm by DLS) as a drug delivery platform have been administered to cancer cells (human breast adenocarcinoma MCF-7 and MDA-MB-231 cell lines) in vitro. Methylene blue, as a model photosensitizer, has been non-covalently bound to FAMs. An increase in photoinduced cytotoxicity has been found upon excitation of the photosensitizer bound to the coating of FAMs compared to the single photosensitizer at equivalent concentrations. The suitability of the nanosystems for photodynamic therapy has been confirmed. Conclusions: FAMs are able to effectively enter cells with increased folate receptor expression and thus allow antitumor photosensitizers to be delivered to cells without any loss of their in vitro photodynamic efficiency. Therapeutic and diagnostic applications of FAMs in oncology are discussed. Full article

Background: Acute myeloid leukemia (AML) is a common and aggressive adults hematological malignancies. This study explored megakaryocyte–erythroid progenitors (MEPs) signature genes and constructed a prognostic model. Methods: Uniform manifold approximation and projection (UMAP) identified distinct cell types, with differential analysis between AML-MEP and normal MEP groups. Univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression selected biomarkers to build a risk model and nomogram for 1-, 3-, and 5-year survival prediction. Results: Ten differentially expressed genes (DEGs) related to overall survival (OS), six (AHSP, MYB, VCL, PIM1, CDK6, as well as SNHG3) were retained post-LASSO. The model exhibited excellent efficiency (the area under the curve values: 0.788, 0.77, and 0.847). Pseudotime analysis of UMAP-defined subpopulations revealed that MYB and CDK6 exert stage-specific regulatory effects during MEP differentiation, with MYB involved in early commitment and CDK6 in terminal maturation. Finally, although VCL, PIM1, CDK6, and SNHG3 showed significant associations with AML survival and prognosis, they failed to exhibit pathological differential expression in quantitative real-time polymerase chain reaction (qRT-PCR) experimental validations. In contrast, the downregulation of AHSP and upregulation of MYB in AML samples were consistently validated by both qRT-PCR and Western blotting, showing the consistency between the transcriptional level changes and protein expression of these two genes (p < 0.05). Conclusions: In summary, the integration of single-cell/transcriptome analysis with targeted expression validation using clinical samples reveals that the combined AHSP-MYB signature effectively identifies high-risk MEP-AML patients, who may benefit from early intensive therapy or targeted interventions. Full article

FMC63-CAR T cell therapy targeting CD19 protein on malignant B-cells is effective in patients with relapsed or refractory diffuse large B-cell lymphoma (r/r DLBCL), with complete response rates of 43–54%. Common germline variants of the immune-checkpoint regulator CTLA-4 may elicit different responses to CAR-T cell therapy. The CTLA4 gene single-nucleotide polymorphism rs231775 coding threonine or alanine at amino acid position 17 of the CTLA-4 protein was prevalent in 55% of the studied DLBCL patients. In a retrospective comparative analysis of clinical outcome, there were significant differences in CTLA4 A17hom vs. T17Ahet and T17hom carriers with four-year progression-free survival at 77%, 59%, and 30% (p = 0.019), four-year overall survival was 79%, 41%, and 33% (p = 0.049), the relapse rates were 20%, 37%, and 56% (p = 0.025), and the death rates 20%, 54%, and 52% (p = 0.049). Conclusions: CTLA4 rs231775 polymorphism may impact the treatment outcome in FMC63-anti-CD19 CAR-T cell therapy, with an association of the CTLA4 minor allele A17 to favorable treatment outcome. Full article

Background: The mechanisms by which sporadic young-onset neurodegenerative processes develop are uncertain. Methods: We have previously proposed that stochastic processes involving sequence changes at a DNA, RNA, or protein level in critical genes and proteins might be important to this process. Further investigation points to the contribution of probabilistic states in other factors involved in neurodegenerative conditions, such as—in the case of young onset Alzheimer’s disease—head injury, apolipoprotein ε4 alleles and other elements that, by the interaction of conditional probabilities in these variables, influence the evolution of neurodegenerative conditions. Results: This proposal might help to explain why some autosomal dominant neurodegenerative conditions, such as trinucleotide repeat disorder (Huntington’s disease), might have variable ages of onset given the same disease-causing CAG repeat mutation length. Conclusions: The detection of somatic mutations in single brain cells provides some experimental support for these emerging concepts. Full article

Background: Rheumatoid arthritis (RA) is an autoimmune disease that remains incurable. An increasing number of proteomic genome-wide association studies (GWASs) are emerging, offering immense potential for identifying novel therapeutic targets for diseases. This study aims to identify potential therapeutic targets for RA based on human plasma proteome. Methods: Protein quantitative trait loci were extracted and integrated from eight large-scale proteomic GWASs. Proteome-wide Mendelian randomization (Pro-MR) was performed to prioritize proteins causally associated with RA. Further validation of the reliability and stratification of prioritized proteins was performed using MR meta-analysis, colocalization, and transcriptome-wide summary-data-based MR. Subsequently, prioritized proteins were characterized through protein–protein interaction and enrichment analyses, pleiotropy assessment, genetically engineered mouse models, cell-type-specific expression analysis, and druggability evaluation. Phenotypic expansion analyses were also conducted to explore the effects of the prioritized proteins on phenotypes such as endocrine disorders, cardiovascular diseases, and other immune-related diseases. Results: Pro-MR prioritized 32 unique proteins associated with RA risk. After validation, prioritized proteins were stratified into four reliability tiers. Prioritized proteins showed interactions with established RA drug targets and were enriched in an immune-related functional profile. Four trans-associated proteins exhibited vertical or horizontal pleiotropy with specific genes or proteins. Genetically engineered mouse models for 18 prioritized protein-coding genes displayed abnormal immune phenotypes. Single-cell RNA sequencing data were used to validate the enriched expression of several prioritized proteins in specific synovial cell types. Nine prioritized proteins were identified as targets of existing drugs in clinical trials or were already approved. Further phenome-wide MR and mediation analyses revealed the effects and potential mediating roles of some prioritized proteins on other phenotypes. Conclusions: This study identified 32 plasma proteins as potential therapeutic targets for RA, expanding the prospects for drug discovery and deepening insights into RA pathogenesis. Full article

In the search for alternative protein sources, single cell proteins have gained increasing attention in recent years. Among them, proteins derived from yeast represent a promising but still underexplored option. To enable their application in food product design, their techno-functional properties must be understood. In order to investigate the impact of precipitation pH on their emulsion-stabilizing properties, yeast proteins from Saccharomyces cerevisiae were isolated via precipitation at different pH (pH 3.5 to 5) after cell disruption in the high-pressure homogenizer. Emulsions containing 5 wt% oil and ~1 wt% protein were analyzed for stability based on their droplet size distribution. Proteins precipitated at pH 3.5 stabilized the smallest oil droplets and prevented partitioning of the emulsion, outperforming proteins precipitated at higher pH values. It is hypothesized that precipitation under acidic conditions induces protein denaturation and thereby exposes hydrophobic regions that enhance adsorption at the oil–water interface and the stabilization of the dispersed oil phase. To investigate the stabilization mechanism, the molecular weight of the proteins was determined using SDS-PAGE, their solubility using Bradford assay, and their aggregation behavior using static laser scattering. Proteins precipitated at pH 3.5 possessed larger molecular weights, lower solubility, and a strong tendency to aggregate. Overall, the findings highlight the potential of yeast-derived proteins as bio-surfactants and suggest that pH-controlled precipitation can tailor their functionality in food formulations. Full article