Search Results (4,414)

Diet plays a central role in shaping the composition and metabolic activity of the oral microbiota, thereby influencing both oral and systemic health. Disturbances in this delicate host–microbe balance, triggered by dietary factors, smoking, poor oral hygiene, or antibiotic use, can lead to microbial dysbiosis and increase the risk of oral diseases such as periodontitis, as well as chronic systemic disorders including diabetes, cardiovascular disease, Alzheimer’s disease, and certain cancers. Among dietary contaminants, exposure to toxic heavy metals such as cadmium (Cd), lead (Pb), mercury (Hg), nickel (Ni), and arsenic (As) represents an underrecognized modifier of the oral microbial ecosystem. Even at low concentrations, these elements can disrupt microbial diversity, promote inflammation, and impair metabolic homeostasis. Saliva has recently emerged as a promising, non-invasive biofluid for monitoring nutritional status and early metabolic alterations induced by diet and environmental exposures. Salivary biomarkers, including metabolites, trace elements, and microbial signatures, offer potential for assessing the combined effects of diet, microbiota, and toxicant exposure. This review synthesizes current evidence on how diet influences the oral microbiota and modulates susceptibility to heavy metal toxicity. It also examines the potential of salivary biomarkers as integrative indicators of nutritional status and metabolic health, highlights methodological challenges limiting their validation, and outlines future research directions for developing saliva-based tools in personalized nutrition and precision health. Full article

Exposure to Particulate Matter (PM) is linked to respiratory and cardiovascular diseases, certain types of cancer, and accounts for approximately seven million premature deaths globally. While governments and organizations have implemented various strategies for Air Quality (AQ) such as the deployment of Air Quality Monitoring Networks (AQMN), these networks often suffer from limited spatial coverage and involve high installation and maintenance costs. Consequently, the implementation of networks based on Low-Cost Sensors (LCS) has emerged as a viable alternative. Nevertheless, LCS systems have certain drawbacks, such as lower reading precision, which can be mitigated through specific calibration models and methods. This paper presents the results and conclusions derived from simultaneous PM₁₀ and PM_2.5 monitoring comparisons between LCS nodes and a T640X reference sensor. Additionally, Relative Humidity (RH), temperature, and absorption flow measurements were collected via an Automet meteorological station. The monitoring equipment was installed at the Faculty of Environment of the Universidad Distrital in Bogotá. The LCS calibration process began with data preprocessing, which involved filtering, segmentation, and the application of FastDTW. Subsequently, calibration was performed using a variety of models, including two statistical approaches, three Machine Learning algorithms, and one Deep Learning model. The findings highlight the critical importance of applying FastDTW during preprocessing and the necessity of incorporating RH, temperature, and absorption flow factors to enhance accuracy. Furthermore, the study concludes that Random Forest and XGBoost offered the highest performance among the methods evaluated. While satellites map city-wide patterns and MAX-DOAS enables hourly source attribution, our calibrated LCS network supplies continuous, street-scale data at low CAPEX/OPEX—forming a practical backbone for sustained micro-scale monitoring in Bogotá. Full article

Background: Current guidelines for NSCLC follow-up lack specific recommendations on surveillance duration. This study aims to analyze survival and surveillance data in resected stage I NSCLC. Methods: We retrospectively reviewed 759 pathological stage I NSCLC (9thTNM ed.) patients with no history of lung cancer (LC) undergoing surgery from January 2003 to December 2018. Overall survival (OS), incidence of relapse (IR), and incidence of new primary LC (NP) were analyzed. Long-term effect of follow-up beyond 5 years was assessed by landmark analysis of OS, IR, and NP at 10 years, restricted to individuals alive without relapse or NP at 5 years (5-year event-free survivors, 5y-EFSs). Results: The rates of 10-year OS, 10-year IR, NP incidence, and 5y-EFSs were, respectively, 75%, 18%, 1.1%/year, and 59.1% (449 patients). Carcinoid IA/IB (0–10%) and adenocarcinoma IA/IB without lung nodules (LNs) (8–12%) had a similarly lower risk of relapse (p = 0.5088) compared to adenocarcinoma with LNs (p = 0.0191). Similarly, carcinoid (0–0.2%/year) and adenocarcinoma without LNs (0-0.3%/year) had the same lower incidence of NP (p = 0.8062) compared to patients with LNs (p < 0.0001). The group of 5y-EFSs had a conditional 10-year OS, IR, and NP incidence of 92%, 5%, and 0.8%/year. In 5y-EFSs, 10-year OS was better in carcinoid (100%) and adenocarcinoma (94%, p = 0.0009) patients; 10-year IR was lower in stage IA (4%) vs. IB (10%, p = 0.0444), and NP was lower in patients with no pre-surgery (0.5 vs. 1.5%/year, p = 0.0147) and no post-surgery LNs (0.6 vs. 1.1%/year, p = 0.0202). Conclusions: Based on our results, we propose a tailored surveillance strategy by de-escalating follow-up for low-risk patients while maintaining intensive monitoring for high-risk individuals. Full article

The escalating incidence of colorectal cancer (CRC), particularly the alarming rise in early-onset cases, necessitates a paradigm shift from a purely genetic perspective to a broader investigation of promising pathways. This review explores the “nutri-epigenetic” interface, positioning liquid biopsy as a critical technology for translating dietary impacts into actionable clinical biomarkers. We contrast the molecular consequences of the Western dietary pattern, characterized by methyl-donor deficiency and pro-inflammatory metabolites, with the protective mechanisms of the Mediterranean diet. Mechanistically, we detail how Western-style diets drive a specific “epigenetic double-hit”: promoting global DNA hypomethylation (destabilizing LINE-1) while paradoxically inducing promoter hypermethylation of critical tumour suppressors (MLH1, APC, MGMT) and silencing tumour-suppressive microRNAs (miR-34b/c, miR-137) via methylation of their encoding genes. Conversely, we highlight the capacity of Mediterranean bioactive compounds (e.g., resveratrol, curcumin, butyrate) to inhibit DNA methyltransferases and restore epigenetic homeostasis. Bridging molecular biology and clinical utility, we demonstrate how these diet-sensitive signatures, specifically circulating methylated DNA and dysregulated microRNAs, can be captured via liquid biopsy. We propose that these circulating analytes serve as dynamic, accessible biomarkers for monitoring the molecular progression toward a carcinogenic state, thereby establishing a novel framework for personalized risk stratification and validating the efficacy of preventive nutritional strategies. Full article

The past decade has witnessed an unprecedented transformation in breast cancer management, driven by parallel advances in targeted therapies, immunomodulation, drug-delivery technologies, and molecular diagnostic tools. This review summarizes the key achievements of 2015–2025, encompassing all major biological subtypes of breast cancer as well as technological innovations with substantial clinical relevance. In hormone receptor-positive (HR+)/HER2− disease, the integration of CDK4/6 inhibitors, modulators of the PI3K/AKT/mTOR pathway, oral Selective Estrogen Receptor Degraders (SERDs), and real-time monitoring of Estrogen Receptor 1 (ESR1) mutations has enabled clinicians to overcome endocrine resistance and dynamically tailor treatment based on evolving molecular alterations detected in circulating biomarkers. In HER2-positive breast cancer, treatment paradigms have been revolutionized by next-generation antibody–drug conjugates, advanced antibody formats, and technologies facilitating drug penetration across the blood–brain barrier, collectively improving systemic and central nervous system disease control. The most rapid progress has occurred in triple-negative breast cancer (TNBC), where synergistic strategies combining selective cytotoxicity via Antibody-Drug Conjugates (ADCs), DNA damage response inhibitors, immunotherapy, epigenetic modulation, and therapies targeting immunometabolic pathways have markedly expanded therapeutic opportunities for this historically challenging subtype. In parallel, photodynamic therapy has emerged as an investigational and predominantly local phototheranostic approach, incorporating nanocarriers, next-generation photosensitizers, and photoimmunotherapy capable of inducing immunogenic cell death and modulating antitumor immune responses. A defining feature of the past decade has been the surge in patent-driven innovation, encompassing multispecific antibodies, optimized ADC architectures, novel linker–payload designs, and advanced nanotechnological and photoactive delivery systems. By integrating data from clinical trials, molecular analyses, and patent landscapes, this review illustrates how multimechanistic, biomarker-guided therapies supported by advanced drug-delivery technologies are redefining contemporary precision oncology in breast cancer. The emerging therapeutic paradigm underscores the convergence of targeted therapy, immunomodulation, synthetic lethality, and localized immune-activating approaches, charting a path toward further personalization of treatment in the years ahead. Full article

Early and accurate diagnosis is critical for disease surveillance, therapeutic guidance, and relapse monitoring. Sensor arrays have emerged as a multi-analyte detection tool via non-specific interactions to generate unique fingerprint patterns with high levels of selectivity and discrimination. Conjugated polymers (CPs), with their tunable π-conjugated backbones, exceptional light-harvesting capability, and efficient “molecular wire effect,” provide an ideal and versatile material platform for such arrays, enabling significant optical signal amplification and high sensitivity. This review systematically outlines the rational design and functionalization strategies of CPs for constructing high-performance sensor arrays. It delves into the structure–property relationships that govern their sensing performance, covering main-chain engineering, side-chain functionalization, and microenvironmental regulation. Representative applications are discussed, including non-small cell lung cancer, breast cancer, bacterial and viral infections, Alzheimer’s disease, and diabetic nephropathy, highlighting the remarkable diagnostic capabilities achieved through tailored CP materials. Finally, future perspectives are focused on novel material designs and device integration to advance this vibrant field. Full article

Endometrial cancer (EC) is the most common gynecological cancer in developed countries, with approximately 417,000 new cases reported worldwide in 2020. Its incidence has been rising for the past 30 years, primarily due to population aging, obesity, and type 2 diabetes; obesity accounts for almost half of cases due to excessive estrogen production. The classic division into types I and II was replaced in 2013 by the molecular TCGA classification, which distinguishes four subtypes: POLE-ultramutated (best prognosis), MSI-hypermutated, copy-number low, and copy-number high (worst prognosis). This classification (refined in ProMisE and TransPORTEC) enables precise treatment: immunotherapy (pembrolizumab, dostarlimab) works excellently in dMMR/MSI-H tumors, PI3K/AKT/mTOR inhibitors and trastuzumab deruxtecan in selected molecular subtypes, and hormone therapy in ER-positive tumors. ctDNA monitoring supports therapeutic decisions. Integrating the molecular profile with FIGO allows for truly personalized treatment, although MMRp/MSS tumors remain a challenge. The future lies in multi-omics, new biomarkers, and combination therapies. Full article

Background and Objectives: Accurate breast tumor segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is crucial for treatment planning, therapy monitoring, and quantitative studies of breast cancer response. However, deep learning models often have worse performance when applied to new hospitals because scanner hardware, acquisition protocols, and patient populations differ from those in the training data. This study investigates how such center-related domain shift affects automated breast DCE-MRI tumor segmentation on the multi-center MAMA-MIA dataset. Methods: We trained a standard 3D U-Net for primary tumor segmentation under two evaluation settings. First, we constructed a random patient-wise split that mixes cases from the three main MAMA-MIA center groups (ISPY2, DUKE, NACT) and used this as an in-distribution reference. Second, we designed a balanced leave-one-center-out cross-validation (LoCoCV) protocol in which each center is held out in turn, while training, validation, and test sets are matched in size across folds. Performance was assessed using the Dice similarity coefficient, 95th percentile Hausdorff distance (HD95), sensitivity, specificity, and related overlap measures. Results: On the mixed-center random split, the best three-channel model achieved a mean Dice of about 0.68 and a mean HD95 of about 19.7 mm on the held-out test set, indicating good volumetric overlap and boundary accuracy when training and test distributions match. Under balanced LoCoCV, the one-channel model reached a mean Dice of about 0.45 and a mean HD95 of about 41 mm on unseen centers, with similar averages for the three-channel variant. Compared with the random split baseline, Dice and sensitivity decreased, while HD95 nearly doubled, showing that boundary errors become larger and segmentations less reliable when the model is applied to new centers. Conclusions: A model that performs well on mixed-center random splits can still suffer a substantial loss of accuracy on completely unseen institutions. The balanced LoCoCV design makes this out-of-distribution penalty visible by separating center-related effects from sample size effects. These findings highlight the need for robust multi-center training strategies and explicit cross-center validation before deploying breast DCE-MRI segmentation models in clinical practice. Full article

The extracellular matrix (ECM) is a dynamic, tissue-specific network that integrates biochemical and mechanical cues to regulate cell behavior and organ homeostasis. Increasing evidence indicates that dysregulated ECM remodeling is an upstream driver of chronic human diseases rather than a passive consequence of injury. This review summarizes principles of ECM organization, mechanotransduction, and pathological remodeling and highlights translational opportunities for ECM-targeted therapies, biomaterial platforms, and precision diagnostics. We conducted a narrative synthesis of foundational and recent literature covering ECM composition and turnover, stiffness-dependent signaling, and disease-associated remodeling across fibrosis/cardiovascular disease, cancer, and metabolic disorders, together with advances in ECM-based biomaterials, drug delivery, and ECMderived biomarkers and imaging. Across organs, a self-reinforcing cycle of altered matrix composition, excessive crosslinking, and stiffness-dependent mechanotransduction (including integrin–FAK and YAP/TAZ pathways) sustains fibroinflammation, myofibroblast persistence, and progressive tissue dysfunction. In tumors, aligned and crosslinked ECM promotes invasion, immune evasion, and therapy resistance while also shaping perfusion and drug penetration. Translational strategies increasingly focus on modulating ECM synthesis and crosslinking, normalizing rather than ablating matrix architecture, and targeting ECM–cell signaling axes in combination with anti-fibrotic, cytotoxic, or immunotherapeutic regimens. ECM biology provides a unifying framework linking pathogenesis, therapy, and precision diagnostics across chronic diseases. Clinical translation will benefit from standardized quantitative measures of matrix remodeling, mechanism-based biomarkers of ECM turnover, and integrative imaging–omics approaches for patient stratification and treatment monitoring. Full article

Nine technology-related metal(loid)s (Ag, Co, Fe, Mo, Pt, Rh, Sb, Se and Y) were monitored in 137 topsoil samples from urban parks, industrial areas and gardens in Alcalá de Henares (Spain) using ICP–MS. Selenium was not detected, while Mo, Sb and Rh showed a high proportion of values below the detection limit, indicating generally low contamination. In contrast, Fe, Co and Y were detected in all samples, with industrial soils showing about two-fold higher median Co and Fe than urban soils. Garden soils displayed marked silver enrichment (median 0.439 vs. 0.068 mg kg⁻¹ in urban soils), with Ag pollution indices up to 71 and enrichment factors up to 69; around 17% of garden samples exceeded EF > 40, and more than one-quarter had EF > 10. Principal component analysis suggested a predominantly geogenic association for Co, Fe and Y and an anthropogenic component for Ag, Mo, Rh and Sb, while Pt was mainly linked to vehicular emissions. Under standard US EPA exposure scenarios applied to the 2001 topsoil concentrations, oral and inhalation hazard quotients for elements with available benchmarks remained <0.2 and inhalation cancer risks for Co were ≤2.5 × 10⁻⁷, indicating low estimated risk within the model assumptions. However, quantitative risk characterisation remains constrained by benchmark gaps for Pt and Rh and by limited consensus toxicity values for Y, which introduces uncertainty for these technology-related elements. These results should therefore be interpreted primarily as a baseline (2001) in surface soils for Alcalá de Henares rather than as a direct representation of current exposure conditions. Full article

This review aims to provide a broad, multidisciplinary perspective on how dynamic genomics and systems biology are transforming modern healthcare, with a focus on cancer especially liver cancer (HCC). It explains how integrating multi-omics technologies such as genomics, transcriptomics, proteomics, interactomics, metabolomics, and spatial transcriptomics deepens our understanding of the complex tumor environment. These innovations enable precise patient stratification based on molecular, spatial, and functional tumor characteristics, allowing for personalized treatment plans. Emphasizing the role of regulatory networks and cell-specific pathways, the review shows how mapping these networks using multi-omics data can predict resistance, identify therapeutic targets, and aid in the development of targeted therapies. The approach shifts from standard, uniform treatments to flexible, real-time strategies guided by technologies such as liquid biopsies and wearable biosensors. A case study showcases the benefits of personalized therapy, which integrates epigenetic modifications, checkpoint inhibitors, and ongoing multi-omics monitoring in a patient with HCC. Future innovations, such as cloud-based genomic ecosystems, federated learning for privacy, and AI-driven data analysis, are also discussed to enhance decision-making and outcomes. The review underscores a move toward predictive and preventive healthcare by integrating layered data into clinical workflows. It reviews ongoing clinical trials using advanced molecular and immunological techniques for HCC. Overall, it promotes a systemic, technological, and spatial approach to cancer treatment, emphasizing the importance of experimental, biochemical–functional, and biophysical data-driven insights in personalizing medicine. Full article

Background: Radiotherapy is a common treatment for gynecological malignancies, often accompanied by significant side effects that impact patient nutritional status. The ketogenic diet has been proposed as a complementary nutritional strategy to enhance treatment efficacy, manage side effects, and preserve body composition. However, its safety and feasibility in the oncological setting remain under-investigated. Methods: The KOMPARC study is a prospective, randomized controlled trial evaluating the adherence, safety, and clinical tolerability of a ketogenic diet versus a standard Mediterranean diet in patients with cervical and endometrial cancer undergoing radiotherapy. Before the start of the treatment, patients were randomized to either the ketogenic diet or the standard diet groups. Anthropometric measures, Hand Grip Test, and body composition parameters from bioimpedance analysis were taken before the start of treatment and at the end. Adherence, adverse events, and patient-reported outcomes were monitored throughout the treatment period. Results: A total of 33 patients were enrolled. Adherence rates were comparable between the KD and standard diet groups (46.1% vs. 25.0% interruption rate, p = 0.21). No significant differences were observed in the incidence of gastrointestinal toxicities (p = 0.56), diarrhea (p = 0.81), nausea (p = 0.94), or weight loss (p = 0.24). Both groups experienced significant weight reduction during therapy without differential loss of body cell mass or other body composition parameters. Quality of life assessments indicated varied symptom profiles, with the KD group reporting increased appetite loss and worry about weight. Conclusions: Preliminary findings suggest that the ketogenic diet is a safe and feasible nutritional intervention during radiotherapy for pelvic tumors. These results support further investigation into ketogenic dietary strategies as adjuncts in oncologic care. Full article

Background: Head and neck squamous cell carcinoma (HNSCC) remains the seventh most common cancer worldwide, characterized by late-stage diagnosis and poor 5-year survival rates. Oral squamous cell carcinoma (OSCC) is the most prevalent subtype. The identification of robust diagnostic, prognostic, and predictive markers is essential for personalized treatment monitoring. Methods: Following PRISMA and PICO standards, we conducted a comprehensive review of studies published over the past 10 years across PubMed/MEDLINE, Scopus, and Web of Science. The selection process was facilitated by AI-powered tools (Rayyan QCRI), and study quality was assessed using NOS or QUIPS. Results: 34 articles (including meta-analyses and original trials) were identified. Established clinical markers, such as p16-positivity (HR ≈ 0.55) and PD-L1 (CPS), remain significant. However, the molecular landscape is expanding to include high-risk lncRNA signatures (HR ≈ 2.50), immune checkpoints such as TIGIT (HR ≈ 1.85), and genomic alterations, including IL-10 promoter polymorphisms. We highlight that epigenetic silencing of p16 affects only about 25% of patients, while metabolic regulators (e.g., GLUT-1) and protein markers (e.g., MASPIN) offer critical predictive value for therapy response. Conclusions: The diagnostic and predictive paradigm is shifting toward a multi-omic approach that integrates DNA, RNA, proteins, and metabolic indicators. Future clinical use will rely on AI-driven multimarker panels and non-invasive liquid biopsies to enable real-time monitoring and de-escalation of treatment strategies. Full article

Polymeric micelles have the potential to improve the efficacy and safety of drug delivery by improving drug solubility, enhancing bioaccumulation and reducing off-target toxicity. Despite excellent safety profiles, a major limitation with polymeric micelles is their inability to rapidly release their payload once they have reached their target, leading to the inadequate delivery of therapeutic doses. To address this limitation, we have developed a novel strategy to impart pH-responsiveness in non-responsive micelles through the co-encapsulation of oligoelectrolytes with drugs. Herein, we investigate the influence of copolymer composition and drug identity in combination with oligoelectrolyte—oligo(2-vinyl pyridine) (OVP)—loading on pH-triggered drug release from micelles and their cytotoxicity. A library of OVP-loaded micelles was prepared using conventional and well-established non-responsive block copolymers. Dynamic light scattering (DLS) was used to monitor the changes in the micelles as a function of pH. Regardless of the copolymer composition, an abrupt decrease in the hydrodynamic diameter (D_h) was observed as the pH was reduced due to OVP expulsion from the core, which was also confirmed by release studies. In general, co-encapsulation of OVP and model drugs (doxorubicin (DOX), gossypol (GP), paclitaxel (PX), and 7-ethyl-10-hydroxycamptothecin (SN38)) in the micelles provided good to excellent encapsulation efficiency percentage (EE%) values. In vitro studies revealed the pH triggered release of drugs from the OVP-loaded micelles regardless of the drug identity, which increased as the OVP loading increased. This general behaviour was observed in all cases, largely independent of the copolymer composition, albeit with subtle differences in the release profile for different drugs. Compared to their blank counterparts, the drug-loaded micelles displayed a slight increase in cytotoxicity against a panel of cancer cell lines, in a dose dependent manner. However, drug- and OVP-loaded micelles displayed a significant increase in cytotoxicity (up to 8-fold increase) that was independent of the copolymer composition. These results demonstrate the versatility of the oligoelectrolyte-mediated approach to furnish non-responsive micelles with a pH-trigger that allows the rapid release of drugs, regardless of the micelle composition or the drug identity. Full article

Background: Clear cell renal cell carcinoma (ccRCC) is the most prevalent kidney cancer subtype and, in most cases, it is incidentally diagnosed, as early-stage disease is often asymptomatic. Therefore, the identification of stable, noninvasive biomarkers is a major unmet clinical need. Urinary microRNAs (miRNAs) have emerged as promising candidates since they are extraordinarily stable in urine and show a close relationship with tumour biology. Methods: In this study, urinary expression levels of five miRNAs (miR-15a, miR-15b, miR-16, miR-210, and miR-let-7b) were analysed in RCC patients before surgery, 5 days after, and one month after surgery, and compared to healthy controls. Results: Non-parametric analyses revealed significant postoperative decreases for miR-15a (p = 0.002), miR-16 (p = 0.025), miR-210 (p = 0.030), and in the overall miRNA Sum (p = 0.002), suggesting that these miRNAs are directly linked to tumour presence. In the comparison between preoperative and one-month postoperative samples, miR-let-7b (p = 0.049) and the global miRNA Sum (p = 0.037) remained significantly reduced after intervention, indicating a partial normalisation of urinary miRNA profiles. Correlation analyses demonstrated positive associations between specific miRNAs and clinical parameters such as age, ischemia time, and surgical time, reinforcing their potential relevance to tumour biology and treatment response. Conclusions: These findings support urinary miRNAs as promising, minimally invasive biomarkers for ccRCC diagnosis and postoperative monitoring. Full article