Search Results (3,173)

Oxidative stress is a defining and pervasive driver of neurodegenerative diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS). As a molecular accelerant, reactive oxygen species (ROS) and reactive nitrogen species (RNS) compromise mitochondrial function, amplify lipid peroxidation, induce protein misfolding, and promote chronic neuroinflammation, creating a positive feedback loop of neuronal damage and cognitive decline. Despite its centrality in promoting disease progression, attempts to neutralize oxidative stress with monotherapeutic antioxidants have largely failed owing to the multifactorial redox imbalance affecting each patient and their corresponding variation. We are now at the threshold of precision redox medicine, driven by advances in syndromic multi-omics integration, Artificial Intelligence biomarker identification, and the precision of patient-specific therapeutic interventions. This paper will aim to reveal a mechanistically deep assessment of oxidative stress and its contribution to diseases of neurodegeneration, with an emphasis on oxidatively modified proteins (e.g., carbonylated tau, nitrated α-synuclein), lipid peroxidation biomarkers (F2-isoprostanes, 4-HNE), and DNA damage (8-OHdG) as significant biomarkers of disease progression. We will critically examine the majority of clinical trial studies investigating mitochondria-targeted antioxidants (e.g., MitoQ, SS-31), Nrf2 activators (e.g., dimethyl fumarate, sulforaphane), and epigenetic reprogramming schemes aiming to re-establish antioxidant defenses and repair redox damage at the molecular level of biology. Emerging solutions that involve nanoparticles (e.g., antioxidant delivery systems) and CRISPR (e.g., correction of mutations in SOD1 and GPx1) have the potential to transform therapeutic approaches to treatment for these diseases by cutting the time required to realize meaningful impacts and meaningful treatment. This paper will argue that with the connection between molecular biology and progress in clinical hyperbole, dynamic multi-targeted interventions will define the treatment of neurodegenerative diseases in the transition from disease amelioration to disease modification or perhaps reversal. With these innovations at our doorstep, the future offers remarkable possibilities in translating network-based biomarker discovery, AI-powered patient stratification, and adaptive combination therapies into individualized/long-lasting neuroprotection. The question is no longer if we will neutralize oxidative stress; it is how likely we will achieve success in the new frontier of neurodegenerative disease therapies. Full article

Cell-free biosensors harness the selectivity of cellular machinery without living cells’ constraints, offering advantages in environmental monitoring, medical diagnostics, and biotechnological applications. This review examines recent advances in cell-free biosensor development, highlighting their ability to detect diverse analytes including heavy metals, organic pollutants, pathogens, and clinical biomarkers with high sensitivity and specificity. We analyze technological innovations in cell-free protein synthesis optimization, preservation strategies, and field deployment methods that have enhanced sensitivity, and practical applicability. The integration of synthetic biology approaches has enabled complex signal processing, multiplexed detection, and novel sensor designs including riboswitches, split reporter systems, and metabolic sensing modules. Emerging materials such as supported lipid bilayers, hydrogels, and artificial cells are expanding biosensor capabilities through microcompartmentalization and electronic integration. Despite significant progress, challenges remain in standardization, sample interference mitigation, and cost reduction. Future opportunities include smartphone integration, enhanced preservation methods, and hybrid sensing platforms. Cell-free biosensors hold particular promise for point-of-care diagnostics in resource-limited settings, environmental monitoring applications, and food safety testing, representing essential tools for addressing global challenges in healthcare, environmental protection, and biosecurity. Full article

Background and Objectives: Accurately diagnosing acute appendicitis (AA) in children remains clinically challenging due to overlapping symptoms with other pediatric conditions and limitations in conventional diagnostic tools. The systemic immune-inflammation index (SII) has emerged as a promising biomarker in adult populations; however, its utility in pediatrics is still unclear. This study aimed to evaluate the diagnostic accuracy of SII in distinguishing pediatric acute appendicitis from elective non-inflammatory surgical procedures and to assess its predictive value in identifying complicated cases. Materials and Methods: This retrospective, single-center study included 397 pediatric patients (5–15 years), comprising 297 histopathologically confirmed appendicitis cases and 100 controls. Demographic and laboratory data were recorded at admission. Inflammatory indices including SII, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) were calculated. ROC curve analysis was performed to evaluate diagnostic performance. Results: SII values were significantly higher in the appendicitis group (median: 2218.4 vs. 356.3; p < 0.001). SII demonstrated excellent diagnostic accuracy for AA (AUROC = 0.95, 95% CI: 0.92–0.97), with 91% sensitivity and 88% specificity at a cut-off > 624. In predicting complicated appendicitis, SII showed moderate discriminative ability (AUROC = 0.66, 95% CI: 0.60–0.73), with 83% sensitivity but limited specificity (43%). Conclusions: SII is a reliable and easily obtainable biomarker for diagnosing pediatric acute appendicitis and may aid in early detection of complicated cases. Its integration into clinical workflows may enhance diagnostic precision, particularly in resource-limited settings. Age-specific validation studies are warranted to confirm its broader applicability. Full article

Background: Oral leukoplakia (OL) and proliferative verrucous leukoplakia (PVL) remain challenging entities due to the absence of reliable prognostic biomarkers. All-trans retinoic acid (atRA), a pivotal modulator of epithelial differentiation and mucosal integrity, has been proposed as a candidate biomarker. This study sought to quantify plasma RA levels in patients with OL and PVL compared to healthy controls, assessing their potential clinical utility. Methods: A cohort of 40 participants was recruited, comprising 10 patients with OL, 10 with PVL, and 20 healthy controls. This nested case–control study was derived from previously characterized institutional databases of oral potentially malignant disorders. Plasma samples were analyzed for atRA concentration using high-precision mass spectrometry. Statistical comparisons were conducted to evaluate differences between groups and associations with clinical outcomes. Results: Patients with homogeneous OL exhibited significantly reduced plasma atRA concentrations (mean 2.17 ± 0.39 pg/mL) relative to both PVL patients (2.64 ± 0.56 pg/mL) and healthy controls (2.66 ± 0.92 pg/mL), with p-values of 0.009 and 0.039, respectively. No statistically significant difference was found between PVL patients and controls. Furthermore, atRA levels demonstrated no correlation with clinicopathological variables or malignant progression within the PVL cohort. Conclusions: These preliminary findings indicate that diminished plasma atRA levels may serve as a prognostic marker for homogeneous oral leukoplakia, whilst its role in PVL appears limited. However, effect estimates were imprecise, and additional studies are warranted. Full article

Glioblastoma (GBM) remains one of the most aggressive and treatment-resistant brain tumors, necessitating novel therapeutic approaches. Oncolytic treatments, particularly oncolytic viruses (OVs), have emerged as promising candidates by selectively infecting and lysing tumor cells while stimulating anti-tumor immunity. Various virus-based therapies are under investigation, including genetically engineered herpes simplex virus (HSV), adenovirus, poliovirus, reovirus, vaccinia virus, measles virus, and Newcastle disease virus, each exploiting unique tumor-selective mechanisms. While some, such as HSV-based therapies including G207 and Delytact^TM, have demonstrated clinical progress, significant challenges persist, including immune evasion, heterogeneity in patient response, and delivery barriers due to the blood–brain barrier. Moreover, combination strategies integrating OVs with immune checkpoint inhibitors, chemotherapy, and radiation are promising but require further clinical validation. Non-viral oncolytic approaches, such as tumor-targeting bacteria and synthetic peptides, remain underexplored. This review highlights current advancements while addressing critical gaps in the literature, including the need for optimized delivery methods, better biomarker-based patient stratification, and a deeper understanding of GBM’s immunosuppressive microenvironment. Future research should focus on enhancing OV specificity, engineering viruses to deliver therapeutic genes, and integrating OVs with precision medicine strategies. By identifying these gaps, this review provides a framework for advancing oncolytic therapies in GBM treatment. Full article

Childhood obesity increases chronic disease risk, but no comprehensive synthesis has evaluated the impact of school-based combined nutrition education and physical activity interventions on cardiometabolic biomarkers in children aged 3 to 12 years. This systematic review was conducted in accordance with PRISMA guidelines and registered in PROSPERO (CRD420251085194). Five databases were systematically searched through June 2025. Twelve randomized controlled trials involving 18,231 children were included and assessed using the PEDro scale. Ten trials demonstrated significant improvements in at least one cardiometabolic biomarker. Blood pressure (8 studies) outcomes showed systolic reductions of 1.41–6.0 mmHg in six studies. Glucose metabolism (5 studies) improved in two studies with reductions of 0.20–0.22 mmol/L. Lipid profiles (7 studies) improved in three studies, including total cholesterol (−0.32 mmol/L). Insulin levels (5 studies) decreased significantly in two investigations. Anthropometric improvements included BMI and body fat. Physical activity increased by >45 min/week and dietary habits improved significantly. Programs with daily implementation (90-min sessions 4x/week), longer duration (≥12 months), family involvement (parent education), and curriculum integration (classroom lessons) showed superior effectiveness. Interventions targeting children with overweight/obesity demonstrated higher changes compared to the general population. However, methodological limitations included a lack of assessor blinding, absence of subject/therapist blinding, and inadequate retention rates. School-based interventions combining nutrition and physical activity can produce significant improvements in cardiometabolic biomarkers, supporting comprehensive, sustained multicomponent programs for early chronic disease prevention. Full article

Background/Objectives: Myelodysplastic syndrome (MDS) is a heterogeneous clonal hematopoietic disorder characterized by ineffective hematopoiesis and leukemic transformation risk. Current therapies show limited efficacy, with ~50% of patients failing hypomethylating agents. This review aims to synthesize recent discoveries through an integrated network model and examine translation into precision therapeutic approaches. Methods: We reviewed breakthrough discoveries from the past three years, analyzing single-cell multi-omics technologies, epitranscriptomics, stem cell architecture analysis, and precision medicine approaches. We examined cell-type-specific splicing aberrations, distinct stem cell architectures, epitranscriptomic modifications, and microenvironmental alterations in MDS pathogenesis. Results: Four interconnected mechanisms drive MDS: genetic alterations (splicing factor mutations), aberrant stem cell architecture (CMP-pattern vs. GMP-pattern), epitranscriptomic dysregulation involving pseudouridine-modified tRNA-derived fragments, and microenvironmental changes. Splicing aberrations show cell-type specificity, with SF3B1 mutations preferentially affecting erythroid lineages. Stem cell architectures predict therapeutic responses, with CMP-pattern MDS achieving superior venetoclax response rates (>70%) versus GMP-pattern MDS (<30%). Epitranscriptomic alterations provide independent prognostic information, while microenvironmental changes mediate treatment resistance. Conclusions: These advances represent a paradigm shift toward personalized MDS medicine, moving from single-biomarker to comprehensive molecular profiling guiding multi-target strategies. While challenges remain in standardizing molecular profiling and developing clinical decision algorithms, this systems-level understanding provides a foundation for precision oncology implementation and overcoming current therapeutic limitations. Full article

In response to the challenges posed by soil degradation in the arid regions of Xinjiang, China, green and organic management practices have emerged as effective alternatives to conventional agricultural management methods, helping to mitigate soil degradation by promoting natural soil recovery and ecological balance. However, most of the existing studies focus on a single management practice or indicator and lack a systematic assessment of the effects of integrated orchard management in arid zones. This study aims to investigate how different agricultural management practices influence soil physicochemical properties and inter-root microbial communities in apple orchards in Xinjiang and to identify the main physicochemical factors affecting the composition of inter-root microbial communities. Inter-root soil samples were collected from apple orchards under green management (GM), organic management (OM), and conventional management (CM) in major apple-producing regions of Xinjiang. Microbial diversity and community composition of the samples were analyzed using high-throughput amplicon sequencing. The results revealed significant differences (p < 0.05) in soil physicochemical properties across different management practices. Specifically, GM significantly reduced soil pH and C:N compared with OM. Both OM and GM significantly decreased soil available nutrient content compared with CM. Moreover, GM and OM significantly increased bacterial diversity and changed the community composition of bacteria and fungi. Proteobacteria and Ascomycota were identified as the dominant bacteria and fungi, respectively, in all management practices. Linear discriminant analysis (LEfSe) showed that biomarkers were more abundant under OM, suggesting that OM may contribute to ecological functions through specific microbial taxa. Co-occurrence network analysis (building a network of microbial interactions) demonstrated that the topologies of bacteria and fungi varied across different management practices and that OM increased the complexity of microbial co-occurrence networks. Mantel test analysis (analyzing soil factors and microbial community correlations) showed that C:N and available potassium (AK) were significantly and positively correlated with the community composition of bacteria and fungi, and that C:N, soil organic carbon (SOC), and alkaline hydrolyzable nitrogen (AN) were significantly and positively correlated with the diversity of fungi. Redundancy analysis (RDA) further indicated that SOC, C:N, and AK were the primary soil physicochemical factors influencing the composition of microbial communities. This study provides theoretical guidance for the sustainable management of orchards in arid zones. Full article

Heart failure (HF) poses a substantial global burden due to its high morbidity, mortality, and healthcare costs. Accurate prognostication is crucial for optimizing treatment, resource allocation, and patient counseling. Prognostic tools range from simple clinical scores such as ADHERE and MAGGIC to more complex models incorporating biomarkers (e.g., NT-proBNP, sST2), imaging, and artificial intelligence techniques. In acute HF, models like EHMRG and STRATIFY aid early triage, while in chronic HF, tools like SHFM and BCN Bio-HF support long-term management decisions. Despite their utility, most models are limited by poor generalizability, reliance on static inputs, lack of integration into electronic health records, and underuse in clinical practice. Novel approaches involving machine learning, multi-omics profiling, and remote monitoring hold promise for dynamic and individualized risk assessment. However, these innovations face challenges regarding interpretability, validation, and ethical implementation. For prognostic models to transition from theoretical promise to practical impact, they must be continuously updated, externally validated, and seamlessly embedded into clinical workflows. This review emphasizes the potential of prognostic models to transform HF care but cautions against uncritical adoption without robust evidence and practical integration. In the evolving landscape of HF management, prognostic models represent a hopeful avenue, provided their limitations are acknowledged and addressed through interdisciplinary collaboration and patient-centered innovation. Full article

Background/Objectives: Androgen deprivation therapy (ADT) is widely used to manage prostate cancer (PC), but the emergence of treatment resistance remains a major clinical challenge. Although the GST family has been implicated in drug resistance, the specific role of GSTM5 remains poorly understood. This study investigates whether GSTM5, alone or in combination with clinical variables, can improve patient stratification based on the risk of early treatment resistance. Methods: In silico analyses were performed to examine GSTM5’s role in protein interactions, molecular pathways, and gene expression. The rs3768490 polymorphism was genotyped in 354 patients with PC, classified by ADT response. Descriptive analysis and logistic regression models were applied to evaluate associations between genotype, clinical variables, and ADT response. GSTM5 expression related to the rs3768490 genotype and ADT response was also analyzed in 129 prostate tissue samples. Results: The T/T genotype of rs3768490 was significantly associated with a lower likelihood of early ADT resistance in both individual (p = 0.0359, Odd Ratios (OR) = 0.18) and recessive models (p = 0.0491, OR = 0.21). High-risk classification according to D’Amico was strongly associated with early progression (p < 0.0004; OR > 5.4). Combining genotype and clinical risk improved predictive performance, highlighting their complementary value in stratifying patients by treatment response. Additionally, GSTM5 expression was slightly higher in T/T carriers, suggesting a potential protective role against ADT resistance. Conclusions: The T/T genotype of rs3768490 may protect against ADT resistance by modulating GSTM5 expression in PC. These preliminary findings highlight the potential of integrating genetic biomarkers into clinical models for personalized treatment strategies, although further studies are needed to validate these observations. Full article

Metabolic syndrome is a global health crisis. However, there are no effective therapeutic strategies for metabolic syndrome. Therefore, this study was conducted to find out a novel silkworm-based metabolic syndrome model that bridges microbial ecology and metabolic dysregulation by integrating hemolymph lipids and midgut microbiota. Our results showed that the levels of HDL-C in the hemolymph of the lean silkworm strain were significantly higher than that in the obese silkworm strain. Furthermore, correlation analysis revealed that Lactococcus and Oceanobacillus were positively related to HDL-C levels, while SM1A02 and Pseudonocardia were negatively associated with HDL-C levels. These relationships between the identified bacteria in the midgut and HDL-C, known as the “good” lipid, in the hemolymph could help guide the development of new treatments for obesity and metabolic problems like high cholesterol in humans. Overall, our results not only established a framework for understanding microbiota-driven lipid dysregulation in silkworms but also offered potential probiotic targets and a bacterial biomarker for obesity and metabolic dysfunction intervention in humans. Full article

Schizophrenia is a challenging multifactorial neuropsychiatric disease that involves interactions between genetic susceptibility and environmental insults. Increasing evidence implicates viral infections as significant environmental contributors, particularly during sensitive neurodevelopmental periods. This review synthesises current findings on the viral hypothesis of schizophrenia, encompassing a wide array of neurotropic viruses, including influenza viruses, herpesviruses (HSV-1 and 2, CMV, VZV, EBV, HHV-6 and 8), hepatitis B and C viruses, HIV, HERVs, HTLV, Zika virus, BoDV, coronaviruses (including SARS-CoV-2), and others. These pathogens can contribute to schizophrenia through mechanisms such as direct microinvasion, persistent central nervous system infection, immune-mediated neuroinflammation, molecular mimicry, and the disturbance of the blood–brain barrier. Prenatal exposure to viral infections can trigger maternal immune activation, resulting in cytokine-mediated alterations in the neurological development of the foetus that persist into adulthood. Genetic studies highlight the role of immune-related loci, including major histocompatibility complex polymorphisms, in modulating susceptibility to infection and neurodevelopmental outcomes. Clinical data also support the “mild encephalitis” hypothesis, suggesting that a subset of schizophrenia cases involve low-grade chronic neuroinflammation. Although antipsychotics have some immunomodulatory effects, adjunctive anti-inflammatory therapies show promise, particularly in treatment-resistant cases. Despite compelling associations, pathogen-specific links remain inconsistent, emphasising the need for longitudinal studies and integrative approaches such as viromics to unravel causal relationships. This review supports a “multi-hit” model in which viral infections interfere with hereditary and immunological susceptibilities, enhancing schizophrenia risk. Elucidating these virus–immune–brain interactions may facilitate the discovery of biomarkers, targeted prevention, and novel therapeutic strategies for schizophrenia. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

Background: Lung cancer remains the leading cause of cancer-related mortality worldwide. Advances in screening, diagnosis, and management have transformed clinical practice, particularly with the integration of immunotherapy and target therapies. Methods: A systematic literature search was carried out for the period between October 2016 to September 2024. Phase II and III randomized trials evaluating ICI monotherapy, ICI–chemotherapy combinations, and dual ICI regimens in patients with advanced NSCLC were included. Outcomes of interest included overall survival (OS), progression-free survival (PFS), and treatment-related adverse events (AEs). Results: PD-1-targeted therapies demonstrated superior OS compared to PD-L1-based regimens, with cemiplimab monotherapyranking highest for OS benefit (posterior probability: 90%), followed by sintilimab plus platinum-based chemotherapy (PBC) and pemetrexed—PBC. PFS atezolizumab plus bevacizumab and PBC, and camrelizumab plus PBC were the most effective regimens. ICI–chemotherapy combinations achieved higher ORRs but were associated with greater toxicity. The most favorable safety profiles were observed with cemiplimab, nivolumab, and avelumab monotherapy, while atezolizumab plus PBC and sugemalimab plus PBC carried the highest toxicity burdens. Conclusions: In PD-L1-unselected advanced NSCLC, PD-1 blockade—particularly cemiplimab monotherapy—and rationally designed ICI–chemotherapy combinations represent the most efficacious treatment strategies. Balancing efficacy with safety remains critical, especially in the absence of predictive biomarkers. These findings support a patient-tailored approach to immunotherapy and highlight the need for further biomarker-driven and real-world investigations to optimize treatment selection. Full article

Affecting over 50% of stroke patients, dysphagia is still challenging to diagnose and manage due to its complex multifactorial nature and can be the result of disruptions in the coordination of cortical and subcortical neural activity as reflected in electroencephalographic (EEG) signal patterns. Sample Entropy (SampEn), a signal complexity or predictability measure, could serve as a tool to identify any abnormalities associated with dysphagia. The present study aimed to identify quantitative dysphagia biomarkers using SampEn from EEG recordings in post-stroke patients. Sample entropy was calculated in the theta, alpha, and beta bands of EEG recordings in a repetitive swallowing task performed by three groups: 22 stroke patients without dysphagia (controls), 36 stroke patients with dysphagia, and 21 healthy age-matched individuals. Post-stroke patients, both with and without dysphagia, exhibited significant differences in SampEn compared to healthy subjects in the alpha and theta bands, suggesting widespread alterations in brain dynamics. These changes likely reflect impairments in sensorimotor integration and cognitive control mechanisms essential for effective swallowing. A significant cluster was identified in the left parietal region during swallowing in the beta band, where dysphagic patients showed higher entropy compared to healthy individuals and controls. This finding suggests altered neural dynamics in a region crucial for sensorimotor integration, potentially reflecting disrupted cortical coordination associated with dysphagia. The precise quantification of these neurophysiological alterations offers a robust and objective biomarker for diagnosing neurogenic dysphagia and monitoring therapeutic interventions by means of EEG, a non-invasive and cost-efficient technique. Full article