Search Results (6,005)

Background: Nowadays, there is a lack of reliable and minimally invasive diagnosis methods for the early detection of Alzheimer’s disease. The development and validation of such tools could significantly reduce the dependence on more invasive and costly confirmatory procedures, such as cerebrospinal fluid biomarkers analysis and neuroimaging techniques. Objectives: The main objective of this study is to validate the clinical performance of a previously developed diagnosis model based on plasma biomarkers from patients in a cognitive disorder unit. Methods: A new cohort of patients was recruited from the same cognitive disorder unit (n = 93). Specifically, demographic data (gender, age, and educational level), plasma biomarkers levels, and genotype (glial fibrillary acidic protein, phosphorylated Tau 181, amyloid-beta42/amyloid-beta40, apolipoprotein E) were collected to evaluate both approaches of the previous diagnosis model (one-cut-off, two-cut-off). Results: The one-cut-off approach showed a sensitivity of 74.3%, a specificity of 89.5%, and an area under the curve of 0.888, while the values for the two-cut-off approach were sensitivity of 66.7%, specificity of 99.9%, and area under the curve of 0.867. Conclusions: A multivariate diagnostic tool was temporally validated for implementation in a clinical unit. In fact, satisfactory results were obtained from both approaches (one-cut-off, two-cut-offs), but the two cut-offs approach was more consistent in correctly identifying non-Alzheimer’s disease cases, allowing us to identify a large number of cases with high specificity. Full article

Sepsis is a complex clinical syndrome associated with high morbidity and mortality and organ dysfunction, most notably acute kidney injury. Early recognition determines crucial clinical decisions for septic individuals. This rapid diagnosis depends on the accuracy of biomarkers in the context of coexisting renal dysfunction. In this context, the value of presepsin has been investigated and challenged for a decade, with no definitive answers. This scoping review aims to evaluate the existing evidence regarding the accuracy of presepsin as a diagnostic and prognostic biomarker for sepsis-associated acute kidney injury (SA-AKI). We obtained 130 articles by searching for references in databases (PubMed/Medline, Web of Science, Embase, and Scopus) following the PRISMA-ScR guidelines. Sequential selection by three independent readers resulted in nine references retained for full analysis. Presepsin demonstrated good diagnostic and prognostic accuracy in patients with AKI, based on observations in small patient groups; however, it requires specific cutoff values, whose determination depends on new controlled and randomized studies. Full article

Background: Colorectal cancer is the third most diagnosed cancer and a leading cause of cancer-related deaths worldwide. Early detection significantly improves patient outcomes, yet many cases are identified only at late stages. The high molecular and genetic heterogeneity of colorectal cancer presents major challenges in accurate diagnosis, prognosis, and therapeutic stratification. Recent advances in gene expression profiling offer new opportunities to discover genes that play a role in colorectal cancer carcinogenesis and may contribute to early diagnosis, prognosis prediction, and the identification of novel therapeutic targets. Methods: This study involved 142 samples: 84 primary tumor samples, 27 liver metastases, and 31 adjacent non-tumor tissues serving as controls. RNA sequencing was performed on a subset of tissues (12 liver metastases and 3 adjacent non-tumor tissues) using a targeted RNA panel covering 395 cancer-related genes. Data processing and differential gene expression analysis were carried out using the DRAGEN RNA and DRAGEN Differential Expression tools. The expression of six genes involved in hypoxia and epithelial-to-mesenchymal transition (EMT) pathways (SLC16A3, ANXA2, P4HA1, SPP1, KRT19, and LGALS3) identified as significantly differentially expressed was validated across the whole cohort via quantitative real-time PCR. The relative expression levels were determined using the ΔΔct method and log2FC, and compared between different groups based on the sample type; clinical parameters; and mutational status of the genes KRAS, PIK3CA, APC, SMAD4, and TP53. Results: Our results suggest that the expression of all the validated genes is significantly altered in metastases compared to non-tumor control samples (p < 0.05). The most pronounced change occurred for the genes P4HA1 and SPP1, whose expression was significantly increased in metastases compared to non-tumor and primary tumor samples, as well as between clinical stages of CRC (p < 0.001). Furthermore, all genes, except for LGALS3, exhibited significantly altered expression between non-tumor samples and samples in stage I of the disease, suggesting that they play a role in the early stages of carcinogenesis (p < 0.05). Additionally, the results suggest the mutational status of the KRAS gene did not significantly affect the expression of any of the validated genes, indicating that these genes are not involved in the carcinogenesis of KRAS-mutated CRC. Conclusions: Based on our results, the genes P4HA1 and SPP1 appear to play a role in the progression and metastasis of colorectal cancer and are candidate genes for further investigation as potential biomarkers in CRC. Full article

This review covers issues related to the characteristics, diagnosis, and treatment of colorectal cancer (CRC). It discusses traditional methods of treating colorectal cancer, including surgery, chemotherapy, and radiotherapy, as well as modern approaches, including targeted therapies, immunotherapy, and innovative gene therapy strategies. Particular attention is paid to the identification of molecular subtypes of CRC, which has revolutionized treatment in advanced stages of the disease and contributed to improved patient survival. The role of biomarkers, including liquid biopsy, in diagnosis, therapy monitoring, and treatment response assessment is emphasized. The potential of artificial intelligence in planning and optimizing surgical procedures is also discussed, opening up new possibilities in personalized therapy. This article provides up-to-date knowledge on the molecular mechanisms of CRC, diagnostic prospects, and directions for the development of precision therapies, serving as a valuable source of information for both clinicians involved in the treatment of CRC and patients wishing to deepen their knowledge of the disease and modern therapeutic options. Full article

Periprosthetic joint infection (PJI) remains a major complication in orthopedic surgery, with accurate and timely diagnosis being essential for optimal patient management. Traditional culture-based diagnostics are often limited by suboptimal sensitivity, especially in biofilm-associated and low-virulence infections. In recent years, non-culture-based methodologies have gained prominence. Molecular techniques, such as polymerase chain reaction (PCR) and next-generation sequencing (NGS), offer enhanced detection of microbial DNA, even in culture-negative cases, and enable precise pathogen identification. In parallel, extensive research has focused on biomarkers, including systemic (e.g., C-reactive protein, fibrinogen, D-dimer), synovial (e.g., alpha-defensin, calprotectin, interleukins), and pathogen-derived markers (e.g., D-lactate), the latter reflecting metabolic products secreted by microorganisms during infection. The development of multiplex platforms now allows for the simultaneous measurement of multiple synovial biomarkers, improving diagnostic accuracy and turnaround time. Furthermore, the integration of artificial intelligence (AI) and machine learning algorithms into diagnostic workflows has opened new avenues for combining clinical, molecular, and biochemical data. These models can generate probability scores for PJI diagnosis with high accuracy, supporting clinical decision-making. While these technologies are still being validated for routine use, their convergence marks a significant step toward precision diagnostics in PJI, potentially improving early detection, reducing diagnostic uncertainty, and guiding targeted therapy. Full article

Background and Objectives: Urothelial carcinoma of the urinary bladder is a heterogeneous disease with diverse morphological and molecular characteristics. This study aims to analyze the expression of HER2 in 100 consecutive cases of muscle-invasive bladder carcinoma (MIBC), with a special attention to the different histological subtypes and consensus molecular variants determined by IHC methods. Materials and Methods: A retrospective single-center study was conducted on 100 consecutive cases of MIBC (2021–2024). HER2 status is assessed by immunohistochemistry (IHC) (scores 0, 0+, 1+, 2+, 3+), and the results are compared with the published data. Results: We have established that over half of the tumors (~60%) show some level of HER2 expression, with strong expression (3+) present in 25%. There are significant differences among the IHC-based molecular variants: luminal tumors, including papillary tumors, exhibit a frequent HER2 overexpression, whereas those with a basal immunophenotype (e.g., squamous, sarcomatoid variants) are almost entirely HER2-negative. The micropapillary subtype and some other rare subtypes can also express HER2. Conclusions: HER2 is an important biomarker with heterogeneous expression in urothelial carcinoma of the bladder. The present study showed that the frequency and level of HER2 expression vary substantially among different histopathological subtypes and molecular variants. In therapeutic terms, interest in HER2 as a target is growing—new antibody–drug conjugates show a promising activity even in cases with low HER2 expression, which will likely lead to the integration of HER2-directed therapies and routine testing in the future. Full article

Background/Objectives: Atopic dermatitis (AD), a chronic inflammatory skin condition, significantly impairs quality of life but remains underdiagnosed in primary care. Blood-cell-count-derived inflammatory indices are emerging as cost-effective biomarkers, but their pathological relevance to AD is limited and requires further discussion. Methods: We developed the Atopic Inflammation Index (AII), a novel blood-cell-based biomarker reflecting AD pathogenesis, and initially assessed its levels in AD patients and healthy controls using clinical samples from Shanghai, China. We then analyzed data from the NHANES (National Health and Nutrition Examination Survey) 2005–2006 cohort (n = 6855) to verify the AII-AD association and compared AII’s diagnostic performance with IgE and eosinophils. Results: Clinical analysis showed a nonlinear association between AII and AD severity. AII effectively distinguished AD patients (including mild cases) from healthy controls (p < 0.001) without elevation in psoriasis or urticaria, unlike eosinophils. In NHANES 2005–2006 (n = 720 AD cases, 10.5%), AII levels were higher in AD compared to non-AD patients (2.33 [1.39–4.09] vs. 2.03 [1.19–3.49], p = 0.007) and remained independently associated after adjustment (OR = 1.03, 95%CI = 1.01–1.04, p = 0.003), while IgE/eosinophils showed non-significant trends. Restricted cubic splines confirmed linear prediction (p = 0.006), and subgroup analyses supported consistency (P-interaction > 0.05). AII outperformed eosinophils (AUC:0.568 vs. 0.546, p = 0.025) with improved detection (sensitivity 0.361→0.614). Sensitivity analysis confirmed robustness after excluding medications, chronic diseases and adult populations. Conclusions: AII is stable and reliable in screening and diagnosing AD, offering a low-cost, practical solution for primary care. This verifies the feasibility of integrating existing detection indicators into new biomarkers, providing valuable inspiration for precision medicine research. Full article

Introduction: Cancer cachexia is associated with systemic inflammation and metabolic derangement, leading to muscle atrophy, which affects over 80% of pancreatic cancer patients, the highest rate among all malignancies, negatively impacting quality of life and significantly reducing survival rate. Malnutrition, skeletal muscle loss (sarcopenia), and imbalanced energy expenditure are indicators of cachexia. No established screening tools in clinical practice are specific and sensitive enough to detect pancreatic cancer in its early stages. Objective: This paper aims to provide new insights by examining contributing factors in the development of cachexia and exploring future directions for managing cachexia to improve quality of life and overall survival in patients with pancreatic cancer. Conclusions: It is clinically vital to identify nutritional risks and consider aggressive nutritional interventions as soon as pancreatic cancer is diagnosed to (1) stabilize body weight, (2) decrease the disease-associated burden, and (3) improve the quality of life. To support the clinical management of cachexia in this population, more research is needed. Specifically, research is needed to identify biomarkers, such as muscle fiber-related genes, optimize drug delivery tailored to the specific metabolic and molecular profile, combine chemotherapeutic agents with nutritional supplements, and consider non-pharmacological interventions such as acupuncture and exercise specifically for cancer-cachexia patients. A multifaceted approach will help achieve a better quality of life and prolonged overall survival in patients with pancreatic cancer. Full article

Aging is a complex, multifactorial process characterized by progressive physiological decline and increased vulnerability to chronic diseases and syndromes. Recent studies have highlighted nine interrelated hallmarks of aging, emerging primarily from an understanding of cellular homeostasis, health, and senescence, such as genomic instability, telomere attrition, and cellular senescence. These hallmarks provide a conceptual framework for advancing pharmacotherapeutic interventions. In this review, we present an overview of old and new pharmacotherapeutic interventions that are being developed using these hallmarks of aging to enhance healthspan delay and ameliorate age-related pathologies. We classify these strategies into five broad categories, including senolytics, senomorphics, NAD⁺ precursors, mTOR inhibitors, and metabolic modifiers, such as metformin, and review the mechanisms by which they act, preclinical evidence for efficacy, and their translational potential to a clinical context. In addition, we consider the clinical landscape and report the important trials that are currently underway and some of the main obstacles, including key challenges such as biomarker identification, safety issues, and regulatory challenges. Overall, we discuss ahead-of-time gerotherapeutics and the important role of a collective, mechanism-focused basis for therapeutically targeting aging biology. Full article

Diabetes mellitus is a chronic metabolic disorder characterised by impaired glucose regulation, leading to severe complications affecting multiple organ systems. Current diagnostic approaches primarily rely on glucose monitoring, which, while being effective, fails to capture the underlying molecular changes associated with disease progression. Emerging biomarkers such as microRNAs (miRNAs) and adipokines offer new insights into diabetes pathophysiology, providing potential diagnostic and prognostic value beyond traditional methods. Given this, precise monitoring of the altered levels of miRNAs and adipokines can forge a path towards early diabetes diagnosis and improved disease management. Biosensors have revolutionised diabetes diagnostics, with glucose biosensors dominating the market for decades. However, recent advancements in nanobiosensors have expanded their scope beyond glucose detection, enabling highly sensitive and selective monitoring of biomolecular markers like miRNAs and adipokines. These nanotechnology-driven platforms offer rapid, inexpensive, and minimally invasive detection strategies, paving the way for improved disease management. This review provides an overview of diabetes, along with its pathogenesis, complications, and demographics, and explores the clinical relevance of miRNAs and adipokines as emerging biomarkers. It further examines the evolution of biosensor technologies, highlights recent developments in nanobiosensors for biomarker detection, and critically analyses the challenges and future directions in this growing field. Full article

Prostate cancer (PCa) diagnosis has historically relied on the prostate-specific antigen (PSA) testing. Although the screening significantly reduces mortality rates, PSA has low specificity with risks of overdiagnosis and overtreatment. These limitations highlight the need for a more accurate diagnostic approach. Emerging technologies, such as artificial intelligence (AI), novel biomarkers, and advanced imaging techniques, offer promising avenues to enhance the accuracy and efficiency of PCa diagnosis and risk stratification. This narrative review comprehensively analyzed the current literature, focusing on new tools aiding PCa diagnosis (AI-driven image interpretation, radiomics, genomic classifiers, biomarkers, and multimodal data integration) with consideration for technical, regulatory, and ethical challenges related to clinical implementation of AI-based technologies. A literature search was performed using the PubMed and MEDLINE databases to identify relevant peer-reviewed articles published in English using the search terms “prostate cancer,” “artificial intelligence,” “machine learning,” “deep learning,” “MRI,” “histopathology,” and “diagnosis.” Articles were selected based on their relevance to AI-assisted diagnostic tools, clinical utility, and performance metrics. In addition, a separate section was developed initially to contextualize the limitations of current PSA-based screening approaches. The reviewed studies showed that AI had significant utility in prostate mpMRI interpretation (lesion detection; Gleason grading) with high accuracy and high reproducibility. For the pathologist, AI-driven algorithms improve the diagnostic accuracy of digital slide evaluation for histologic diagnosis of prostate cancer and automated Gleason score grading. Genomic tools such as the Oncotype DX test, combined with AI, could also allow for tailored and individualized risk prediction. Overall, multimodal models integrating clinical, imaging, and molecular data often outperform traditional PSA-based strategies and reduce unnecessary biopsies. Transition from PSA-centered toward AI-driven, biomarker-supported, and image-enhanced diagnosis marks a critical evolution in PCa diagnosis. Full article

Acute kidney injury (AKI) is a frequent and severe complication in trauma patients, affecting up to 28% of intensive care unit (ICU) admissions and contributing significantly to morbidity, mortality, and long-term renal impairment. Trauma-related AKI (TRAKI) arises from diverse mechanisms, including hemorrhagic shock, ischemia–reperfusion injury, systemic inflammation, rhabdomyolysis, nephrotoxicity, and complex organ crosstalk involving the brain, lungs, and abdomen. Pathophysiologically, TRAKI involves early disruption of the glomerular filtration barrier, tubular epithelial injury, and renal microvascular dysfunction. Inflammatory cascades, oxidative stress, immune thrombosis, and maladaptive repair mechanisms mediate these injuries. Trauma-related rhabdomyolysis and exposure to contrast agents or nephrotoxic drugs further exacerbate renal stress, particularly in patients with pre-existing comorbidities. Traditional markers such as serum creatinine (sCr) are late indicators of kidney damage and lack specificity. Emerging structural and stress response biomarkers—such as neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule 1 (KIM-1), liver-type fatty acid-binding protein (L-FABP), interleukin-18 (IL-18), C-C motif chemokine ligand 14 (CCL14), Dickkopf-3 (DKK3), and the U.S. Food and Drug Administration (FDA)-approved tissue inhibitor of metalloproteinases-2 × insulin-like growth factor-binding protein 7 (TIMP-2 × IGFBP-7)—allow earlier detection of subclinical AKI and better predict progression and the need for renal replacement therapy. Together, functional indices like urinary sodium and fractional potassium excretion reflect early microcirculatory stress and add clinical value. In parallel, risk stratification tools, including the Renal Angina Index (RAI), the McMahon score, and the Haines model, enable the early identification of high-risk patients and help tailor nephroprotective strategies. Together, these biomarkers and risk models shift from passive AKI recognition to proactive, personalized management. A new paradigm that integrates biomarker-guided diagnostics and dynamic clinical scoring into trauma care promises to reduce AKI burden and improve renal outcomes in this critically ill population. Full article

α-L-fucosidases (EC 3.2.1.51) are of particular interest due to their ability to cleave terminal α-L-fucose residues from glycoconjugates, a property associated with numerous biological and therapeutic effects. They have also been investigated for their potential use in glycan remodeling, disease biomarker analysis, and particularly as therapeutic agents in the context of fucosidosis, a rare lysosomal storage disorder, caused by a deficiency in α-L-fucosidase activity. However, limitations in enzyme availability, stability, and substrate specificity highlight the need for novel and more efficient enzyme sources. Bifidobacterium castoris (B. castor is) is a newly identified species first discovered in the beaver gut microbiota in 2019. Phylogenetic studies have revealed its advanced metabolic capacity, and genomic analyses have demonstrated its extensive carbohydrate metabolism potential. This research article focuses on the recombinant production and biochemical characterization of a novel α-L-fucosidase from B. castoris LMG (Laboratorium voor Microbiologie Gent) 30937, predicted to belong to glycoside hydrolase family 29 (GH29) according to Universal Protein Resource (UniProt) annotation. Under optimized reaction conditions the recombinant α-L-fucosidase exhibited a specific activity of 0.264 U/mg to pNP-Fuc (4-Nitrophenyl-α-L-fucopyranoside). The results indicate that the enzyme is active in the pH range of 3.0–8.0 and temperatures of 24–42 °C, but its optimum conditions are the slightly acidic pH of 5.5 and the elevated temperature of 42 °C. This profile suggests that the enzyme is adapted to acidic intestinal-like environments. This novel enzyme expands the GH29 α-L-fucosidase repertoire and offers a promising new candidate for future biotechnological applications. Full article

DROSHA protein is widely known for its essential role in the microRNA (miRNA/miR) biogenesis pathway where, together with its co-factor DGCR8, it forms the “Microprocessor” complex and catalyzes the primary miRNA (pri-miRNA) processing in the nucleus. Nevertheless, DROSHA also seems to participate in several miRNA-independent cellular mechanisms, such as transcriptional regulation, RNA processing and genome integrity maintenance. Hence, the present study aims to further investigate novel miRNA-independent activities of DROSHA protein, with potentially regulatory roles in the oncogenesis of human cancer cells. Our results reveal a new, strong profile of microprocessor-independent DROSHA localization at the Golgi apparatus in several human cancer cell lines of different tissue origin, with hepatic carcinoma, thyroid cancer, urothelial bladder cancer, colon carcinoma and melanoma being the cellular model systems herein examined. Notably, oncogenic activity, malignancy grade and metastatic capacity are shown to be strongly associated with DROSHA’s compartmentalization at Golgi, a phenotype that does not seem to rely on p53 protein’s functionality. Taken together, through employment of advanced confocal laser scanning microscopy (CLSM) and molecular modeling, we herein unveil the ability of DROSHA, but not AGO2 and DICER, to reside at Golgi, where DROSHA can physically interact with the GM130 Golgi-specific component, thus indicating DROSHA’s engagement in non-canonical and miRNA-independent—but also Golgi apparatus-dependent—novel mechanisms that can be tightly coupled with malignancy dynamics and beneficially utilized as potential biomarkers and therapeutic targets for human cancer. Full article

(1) Background: The convergence of digital twin technology, artificial intelligence, and multimodal biomarkers heralds a transformative era in neuropsychological assessment and intervention. Digital twin cognition represents an emerging paradigm that creates dynamic, personalized virtual models of individual cognitive systems, enabling continuous monitoring, predictive modeling, and precision interventions. This systematic review comprehensively examines the integration of AI-driven biomarkers within biomimetic neuropsychological frameworks to advance personalized cognitive health. (2) Methods: Following PRISMA 2020 guidelines, we conducted a systematic search across six major databases spanning medical, neuroscience, and computer science disciplines for literature published between 2014 and 2024. The review synthesized evidence addressing five research questions examining framework integration, predictive accuracy, clinical translation, algorithm effectiveness, and neuropsychological validity. (3) Results: Analysis revealed that multimodal integration approaches combining neuroimaging, physiological, behavioral, and digital phenotyping data substantially outperformed single-modality assessments. Deep learning architectures demonstrated superior pattern recognition capabilities, while traditional machine learning maintained advantages in interpretability and clinical implementation. Successful frameworks, particularly for neurodegenerative diseases and multiple sclerosis, achieved earlier detection, improved treatment personalization, and enhanced patient outcomes. However, significant challenges persist in algorithm interpretability, population generalizability, and the integration of healthcare systems. Critical analysis reveals that high-accuracy claims (85–95%) predominantly derive from small, homogeneous cohorts with limited external validation. Real-world performance in diverse clinical settings likely ranges 10–15% lower, emphasizing the need for large-scale, multi-site validation studies before clinical deployment. (4) Conclusions: Digital twin cognition establishes a new frontier in personalized neuropsychology, offering unprecedented opportunities for early detection, continuous monitoring, and adaptive interventions while requiring continued advancement in standardization, validation, and ethical frameworks. Full article