Search Results (401)

Liquid biopsy, a noninvasive diagnosis that examines circulating tumor components in body fluids, is increasingly used in cancer management. An overview of relevant literature emphasizes the current state of liquid biopsy applications in cancer care. Biomarkers in liquid biopsy, particularly circulating tumor DNA (ctDNA), circulating tumor RNAs (ctRNA), circulating tumor cells (CTCs), extracellular vesicles (EVs), and other components, offer promising opportunities for early cancer diagnosis, treatment selection, monitoring, and disease assessment. The implementation of liquid biopsy in precision medicine has shown significant potential in various cancer types, including lung cancer, colorectal cancer, breast cancer, and prostate cancer. Advances in genomic and molecular technologies such as next-generation sequencing (NGS) and digital polymerase chain reaction (dPCR) have expanded the utility of liquid biopsy, enabling the detection of somatic variants and actionable genomic alterations in tumors. Liquid biopsy has also demonstrated utility in predicting treatment responses, monitoring minimal residual disease (MRD), and assessing tumor heterogeneity. Nevertheless, standardizing liquid biopsy techniques, interpreting results, and integrating them into the clinical routine remain as challenges. Despite these challenges, liquid biopsy has significant clinical implications in cancer management, offering a dynamic and noninvasive approach to understanding tumor biology and guiding personalized treatment strategies. Full article

Diets, but also overall food environments, comprise a variety of significant factors with direct and indirect impacts on human health. Eco-Regions are geographical areas with a territorial approach to rural development, utilizing organic food and farming practices, and principles and promoting sustainable communities and food systems. However, so far, little attention has been given to quantifying aspects of the health of citizens living in these sustainable transition territories. The project “Indicators for Assessment of Health Effects of Consumption of Sustainable, Organic School Meals in Eco-Regions” (INSUM) aims to identify and discuss research approaches and indicators that could be applied to effectively measure the somatic, mental, and social health dimensions of citizens in Eco-Regions, linked to the intake of organic foods in their diets. In this paper, we focus on the somatic (physical) health dimension. A two-day workshop was held to discuss suitable methodology with an interdisciplinary, international group of experts. The results showed the limitations of commonly used tools for measuring dietary intake (e.g., relying on the memory of participants), and nutritional biomarkers (e.g., variations in correlations with specific intakes) for research understanding dietary intake and the health effects of diets. To investigate the complexity of this issue, the most suitable approach seems to be the combination of traditional markers of physical and mental health alongside emerging indicators such as the microbiome, nutrigenomics, metabolomics, or inflammatory biomarkers. Using new, digital, non-invasive, and wearable technologies to monitor indicators could complement future research. We conclude that future studies should adopt systemic, multidisciplinary approaches by combining not only indicators of somatic and mental health and social wellbeing (MHSW) but also considering the potential benefits of organic diets for health as well as aspects of sustainability connected to food environments. Full article

This literature review explores advancements in obesity and diabetes mellitus diagnosis and treatment, highlighting recent innovations that promise more personalized and effective healthcare interventions. For obesity diagnosis, traditional methods like body mass index (BMI) calculations are now complemented by bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) scans, with emerging biomarkers from “omics” technologies. Diabetes diagnosis has advanced with standard hemoglobin A1c (HbA1c) testing supplemented by novel measures such as advanced glycation end products (AGEs) and autoantibodies, alongside the use of artificial intelligence to enhance diagnostic accuracy. Treatment options for obesity are expanding beyond traditional methods. Minimally invasive bariatric surgeries, endoscopic procedures, fecal microbiota transplants (FMTs), and pharmaceuticals like GLP-1 receptor agonists (semaglutide, tirzepatide) show promising results. Cognitive behavioral therapy (CBT) and prescription digital therapeutics (PDTs) are also valuable tools for weight management. Diabetes treatment is also undergoing a transformation. Ultra-long-acting insulins and innovative oral insulin delivery methods are on the horizon. SGLT2 inhibitors and GLP-1 receptor agonists are proving to be effective medications for blood sugar control. Continuous glucose monitoring (CGM) systems and closed-loop insulin delivery are revolutionizing diabetes management, while stem cell therapy holds promise for the future. By integrating advanced diagnostic tools with personalized treatment plans, obesity and diabetes care are entering a new era. This personalized approach empowers patients and paves the way for improved health outcomes and a better quality of life. Full article

Breast cancer (BC) is the most common cancer in women, with incidence rates increasing globally in recent years. Therefore, it is important to find new molecules with prognostic and therapeutic value to improve therapeutic response and quality of life. The polyunsaturated fatty acids (PUFAs) metabolic pathway participates in various physiological processes, as well as in the development of malignancies. Although aberrancies in the PUFAs metabolic pathway have been implicated in carcinogenesis, the functional and clinical relevance of this pathway has not been well explored in BC. To evaluate the clinical significance of soluble epoxide hydrolase (EPHX2) expression in Mexican patients with BC using tissue microarrays (TMAs) and digital pathology (DP). Immunohistochemical analyses were performed on 11 TMAs with 267 BC samples to quantify this enzyme. Using DP, EPHX2 protein expression was evaluated solely in tumor areas. The association of EPHX2 with overall survival (OS) was detected through bioinformatic analysis in public databases and confirmed in our cohort via Cox regression analysis. Clear nuclear expression of EPHX2 was identified. Receiver operating characteristics (ROC) curves revealed the optimal cutoff point at 2.847062 × 10⁻³ pixels, with sensitivity of 69.2% and specificity of 67%. Stratification based on this cutoff value showed elevated EPHX2 expression in multiple clinicopathological features, including older age and nuclear grade, human epidermal growth factor receptor 2 (HER2) and triple negative breast cancer (TNBC) subtypes, and recurrence. Kaplan–Meier curves demonstrated how higher nuclear expression of EPHX2 predicts shorter OS. Consistently, multivariate analysis confirmed EPHX2 as an independent predictor of OS, with a hazard ratio (HR) of 3.483 and a 95% confidence interval of 1.804–6.724 (p < 0.001). Our study demonstrates for the first time that nuclear overexpression of EPHX2 is a predictor of poor prognosis in BC patients. The DP approach was instrumental in identifying this significant association. Our study provides valuable insights into the potential clinical utility of EPHX2 as a prognostic biomarker and therapeutic target in BC. Full article

The steady progress in consumer electronics, together with improvement in microflow techniques, nanotechnology, and data processing, has led to implementation of cost-effective, user-friendly portable devices, which play the role of not only gadgets but also diagnostic tools. Moreover, numerous smart devices monitor patients’ health, and some of them are applied in point-of-care (PoC) tests as a reliable source of evaluation of a patient’s condition. Current diagnostic practices are still based on laboratory tests, preceded by the collection of biological samples, which are then tested in clinical conditions by trained personnel with specialistic equipment. In practice, collecting passive/active physiological and behavioral data from patients in real time and feeding them to artificial intelligence (AI) models can significantly improve the decision process regarding diagnosis and treatment procedures via the omission of conventional sampling and diagnostic procedures while also excluding the role of pathologists. A combination of conventional and novel methods of digital and traditional biomarker detection with portable, autonomous, and miniaturized devices can revolutionize medical diagnostics in the coming years. This article focuses on a comparison of traditional clinical practices with modern diagnostic techniques based on AI and machine learning (ML). The presented technologies will bypass laboratories and start being commercialized, which should lead to improvement or substitution of current diagnostic tools. Their application in PoC settings or as a consumer technology accessible to every patient appears to be a real possibility. Research in this field is expected to intensify in the coming years. Technological advancements in sensors and biosensors are anticipated to enable the continuous real-time analysis of various omics fields, fostering early disease detection and intervention strategies. The integration of AI with digital health platforms would enable predictive analysis and personalized healthcare, emphasizing the importance of interdisciplinary collaboration in related scientific fields. Full article

Ulnar collateral ligament (UCL) tears occur due to the prolonged exposure and overworking of joint stresses, resulting in decreased strength in the flexion and extension of the elbow. Current rehabilitation approaches for UCL tears involve subjective assessments (pain scales) and objective measures such as monitoring joint angles and range of motion. The main goal of this study is to find out if using wearable near-infrared spectroscopy technology can help measure digital biomarkers like muscle oxygen levels and heart rate. These measurements could then be applied to athletes who have been injured. Specifically, measuring muscle oxygen levels will help us understand how well the muscles are using oxygen. This can indicate improvements in how the muscles are healing and growing new blood vessels after reconstructive surgery. Previous research studies demonstrated that there remains an unmet clinical need to measure biomarkers to provide continuous, internal data on muscle physiology during the rehabilitation process. This study’s findings can benefit team physicians, sports scientists, athletic trainers, and athletes in the identification of biomarkers to assist in clinical decisions for optimizing training regimens for athletes that perform overarm movements; the research suggests pathways for possible earlier detection, and thus earlier intervention for injury prevention. Full article

Conventional diagnostic tools for prostate cancer (PCa), such as prostate-specific antigen (PSA), transrectal ultrasound (TRUS), digital rectal examination (DRE), and tissue biopsy face, limitations in individual risk stratification due to invasiveness or reliability issues. Liquid biopsy is a less invasive and more accurate alternative. Metabolomic analysis of extracellular vesicles (EVs) holds a promise for detecting non-genetic alterations and biomarkers in PCa diagnosis and risk assessment. The current research gap in PCa lies in the lack of accurate biomarkers for early diagnosis and real-time monitoring of cancer progression or metastasis. Establishing a suitable approach for observing dynamic EV metabolic alterations that often occur earlier than being detectable by other omics technologies makes metabolomics valuable for early diagnosis and monitoring of PCa. Using four distinct metabolite extraction approaches, the metabolite cargo of PC3-derived large extracellular vesicles (lEVs) was evaluated using a combination of methanol, cell shearing using microbeads, and size exclusion filtration, as well as two fractionation chemistries (pHILIC and C18 chromatography) that are also examined. The unfiltered methanol–microbeads approach (MB-UF), followed by pHILIC LC-MS/MS for EV metabolite extraction and analysis, is effective. Identified metabolites such as L-glutamic acid, pyruvic acid, lactic acid, and methylmalonic acid have important links to PCa and are discussed. Our study, for the first time, has comprehensively evaluated the extraction and separation methods with a view to downstream sample integrity across omics platforms, and it presents an optimised protocol for EV metabolomics in PCa biomarker discovery. Full article

Prostate cancer (PC) is a prevalent and potentially fatal form of cancer that affects men globally. However, the existing diagnostic methods, such as biopsies or digital rectal examination (DRE), have limitations in terms of invasiveness, cost, and accuracy. This study proposes a novel machine learning approach for the diagnosis of PC by leveraging clinical biomarkers and personalized questionnaires. In our research, we explore various machine learning methods, including traditional, tree-based, and advanced tabular deep learning methods, to analyze tabular data related to PC. Additionally, we introduce the novel utilization of convolutional neural networks (CNNs) and transfer learning, which have been predominantly applied in image-related tasks, for handling tabular data after being transformed to proper graphical representations via our proposed Tab2Visual modeling framework. Furthermore, we investigate leveraging the prediction accuracy further by constructing ensemble models. An experimental evaluation of our proposed approach demonstrates its effectiveness in achieving superior performance attaining an F1-score of 0.907 and an AUC of 0.911. This offers promising potential for the accurate detection of PC without the reliance on invasive and high-cost procedures. Full article

Tissue biopsy remains the standard for diagnosing gastrointestinal stromal tumors (GISTs), although liquid biopsy is emerging as a promising alternative in oncology. In this pilot study, we advocate for droplet digital PCR (ddPCR) to diagnose GIST in tissue samples and explore its potential for early diagnosis via liquid biopsy, focusing on the PDGFRA D842V mutation and SEPT9 hypermethylated gene. We utilized ddPCR to analyze the predominant PDGFRA mutation (D842V) in surgical tissue samples from 15 GIST patients, correlating with pathologists’ diagnoses. We expanded our analysis to plasma samples to compare DNA alterations between tumor tissue and plasma, also investigating SEPT9 gene hypermethylation. We successfully detected the PDGFRA D842V mutation in GIST tissues by ddPCR. Despite various protocols to enhance mutation detection in early-stage disease, it remained challenging, likely due to the low concentration of DNA in plasma samples. Additionally, the results of Area Under the Curve (AUC) for the hypermethylated SEPT9 gene, analyzing concentration, ratio, and abundance were 0.74 (95% Confidence Interval (CI): 0.52 to 0.97), 0.77 (95% CI: 0.56 to 0.98), and 0.79 (95% CI: 0.59 to 0.99), respectively. As a rare disease, the early detection of GIST through such biomarkers is particularly crucial, offering significant potential to improve patient outcomes. Full article

Background: The origin of pre-existing cognitive impairment in stroke patients remains controversial, with a vascular or a degenerative hypothesis. Objective: To determine whether endothelial dysfunction is associated with pre-existing cognitive problems, lesion load and biological anomalies in stroke patients. Methods: Patients originated from the prospective STROKDEM study. The baseline cognitive state, assessed using the IQ-CODE, and risk factors for stroke were recorded at inclusion. Patients with an IQ-CODE score >64 were excluded. Endothelial function was determined 72 h after stroke symptom onset by non-invasive digital measurement of endothelium-dependent flow-mediated dilation and calculation of the reactive hyperemia index (RHI). RHI ≤ 1.67 indicated endothelial dysfunction. Different biomarkers of endothelial dysfunction were analysed in blood or plasma. All patients underwent MRI 72 h after stroke symptom onset. Results: A total of 86 patients were included (52 males; mean age 63.5 ± 11.5 years). Patients with abnormal RHI have hypertension or antihypertensive treatment more often. The baseline IQ-CODE was abnormal in 33 (38.4%) patients, indicating a pre-existing cognitive problem. Baseline IQ-CODE > 48 was observed in 15 patients (28.3%) with normal RHI and in 18 patients (54.6%) with abnormal RHI (p = 0.016). The RHI median was significantly lower in patients with abnormal IQ-CODE. Abnormal RHI was associated with a significantly higher median FAZEKAS score (2.5 vs. 2; p = 0.008), a significantly higher frequency of periventricular lesions (p = 0.015), more white matter lesions (p = 0.007) and a significantly higher cerebral atrophy score (p < 0.001) on MRI. Vascular biomarkers significantly associated with abnormal RHI were MCP-1 (p = 0.009), MIP_1a (p = 0.042), and homocysteinemia (p < 0.05). Conclusions: A vascular mechanism may be responsible for cognitive problems pre-existing stroke. The measurement of endothelial dysfunction after stroke could become an important element of follow-up, providing an indication of the functional and cognitive prognosis of stroke patients. Full article

Pleural mesothelioma (PM) is a highly aggressive tumor that is caused by asbestos exposure and lacks effective therapeutic regimens. Current procedures for PM diagnosis are invasive and can take a long time to reach a definitive result. Small extracellular vesicles (sEVs) have been identified as important communicators between tumor cells and their microenvironment via their cargo including circular RNAs (circRNAs). CircRNAs are thermodynamically stable, highly conserved, and have been found to be dysregulated in cancer. This study aimed to identify potential biomarkers for PM diagnosis by investigating the expression of specific circRNA gene pattern (hsa_circ_0007386) in cells and sEVs using digital polymerase chain reaction (dPCR). For this reason, 5 PM, 14 non-PM, and one normal mesothelial cell line were cultured. The sEV was isolated from the cells using the gold standard ultracentrifuge method. The RNA was extracted from both cells and sEVs, cDNA was synthesized, and dPCR was run. Results showed that hsa_circ_0007386 was significantly overexpressed in PM cell lines and sEVs compared to non-PM and normal mesothelial cell lines (p < 0.0001). The upregulation of hsa_circ_0007386 in PM highlights its potential as a diagnostic biomarker. This study underscores the importance and potential of circRNAs and sEVs as cancer diagnostic tools. Full article

Background: Multiple sclerosis (MS) is a clinically heterogeneous disease underpinned by inflammatory, demyelinating and neurodegenerative processes, the extent of which varies between individuals and over the course of the disease. Recognising the clinicopathological features that most strongly associate with disease outcomes will inform future efforts at patient phenotyping. Aims: We used a digital pathology workflow, involving high-resolution image acquisition of immunostained slides and opensource software for quantification, to investigate the relationship between clinical and neuropathological features in an autopsy cohort of progressive MS. Methods: Sequential sections of frontal, cingulate and occipital cortex, thalamus, brain stem (pons) and cerebellum including dentate nucleus (n = 35 progressive MS, females = 28, males = 7; age died = 53.5 years; range 38–98 years) were immunostained for myelin (anti-MOG), neurons (anti-HuC/D) and microglia/macrophages (anti-HLA). The extent of demyelination, neurodegeneration, the presence of active and/or chronic active lesions and quantification of brain and leptomeningeal inflammation was captured by digital pathology. Results: Digital analysis of tissue sections revealed the variable extent of pathology that characterises progressive MS. Microglia/macrophage activation, if found at a higher level in a single block, was typically elevated across all sampled blocks. Compartmentalised (perivascular/leptomeningeal) inflammation was associated with age-related measures of disease severity and an earlier death. Conclusion: Digital pathology identified prognostically important clinicopathological correlations in MS. This methodology can be used to prioritise the principal pathological processes that need to be captured by future MS biomarkers. Full article

The adequacy and efficacy of simple and hybrid machine learning and Computational Intelligence algorithms were evaluated for the classification of potential prostate cancer patients in two distinct categories, the high- and the low-risk group for PCa. The evaluation is based on randomly generated surrogate data for the biomarker PSA, considering that reported epidemiological data indicated that PSA values follow a lognormal distribution. In addition, four more biomarkers were considered, namely, PSAD (PSA density), PSAV (PSA velocity), PSA ratio, and Digital Rectal Exam evaluation (DRE), as well as patient age. Seven simple classification algorithms, namely, Decision Trees, Random Forests, Support Vector Machines, K-Nearest Neighbors, Logistic Regression, Naïve Bayes, and Artificial Neural Networks, were evaluated in terms of classification accuracy. In addition, three hybrid algorithms were developed and introduced in the present work, where Genetic Algorithms were utilized as a metaheuristic searching technique in order to optimize the training set, in terms of minimizing its size, to give optimal classification accuracy for the simple algorithms including K-Nearest Neighbors, a K-means clustering algorithm, and a genetic clustering algorithm. Results indicated that prostate cancer cases can be classified with high accuracy, even by the use of small training sets, with sizes that could be even smaller than 30% of the dataset. Numerous computer experiments indicated that the proposed training set minimization does not cause overfitting of the hybrid algorithms. Finally, an easy-to-use Graphical User Interface (GUI) was implemented, incorporating all the evaluated algorithms and the decision-making procedure. Full article

In recent years, in the field of bioanalysis, the use of saliva as a biological fluid for the determination of biomarkers has been proposed. Saliva analysis stands out for its simplicity and non-invasive sampling. This paper proposes a method for the dual determination of ammonium and hydrogen sulfur in saliva using two colorimetric chemosensors. The ammonia reacts with 1,2-Naftoquinone 4 sulphonic acid (NQS) entrapped in polydimethylsiloxane (PDMS) and the hydrogen sulfide with AgNPs retained in a nylon membrane. The color changed from orange to brown in the case of ammonia chemosensors and from yellow to brown in the H₂S. The experimental conditions to be tested have been established. Both analytes have been determined from their gaseous form; these are ammonia from ammonium and hydrogen sulfur from hydrogen sulfur. Good figures of merit have been obtained by using both measuring strategies (reflectance diffuse and digitalized images). The acquired results show that both sensors can be used and provide good selectivity and sensitivity for the determination of these biomarkers in saliva. Both measurement strategies have provided satisfactory results for the real saliva samples (n = 15). Recoveries on spiked samples were between 70% and 100%. This methodology can lead to possible in situ diagnosis and monitoring of certain diseases and pathologies related with NH₄⁺ and/or H₂S, in a fast, simple, cheap and non-invasive way. Full article

Background: The endometrium remains a difficult tissue for the analysis of microbiota, mainly due to the low bacterial presence and the sampling procedures. Among its pathologies, endometrial cancer has not yet been completely investigated for its relationship with microbiota composition. In this work, we report on possible correlations between endometrial microbiota dysbiosis and endometrial cancer. Methods: Women with endometrial cancer at various stages of tumor progression were enrolled together with women with a benign polymyomatous uterus as the control. Analyses were performed using biopsies collected at two specific endometrial sites during the surgery. This study adopted two approaches: the absolute quantification of the bacterial load, using droplet digital PCR (ddPCR), and the analysis of the bacterial composition, using a deep metabarcoding NGS procedure. Results: ddPCR provided the first-ever assessment of the absolute quantification of bacterial DNA in the endometrium, confirming a generally low microbial abundance. Metabarcoding analysis revealed a different microbiota distribution in the two endometrial sites, regardless of pathology, accompanied by an overall higher prevalence of pathogenic bacterial genera in cancerous tissues. Conclusions: These results pave the way for future studies aimed at identifying potential biomarkers and gaining a deeper understanding of the role of bacteria associated with tumors. Full article