Search Results (1,529)

In digital pathology, accurate diagnosis and prognosis critically depend on robust feature representation of Whole Slide Images (WSIs). While deep learning offers powerful solutions, its “black box” nature presents significant challenges to clinical interpretability and widespread adoption. Handcrafted features offer interpretability, yet orthogonal moments, particularly Tchebichef moments (TMs), remain underexplored for WSI analysis. This study introduces TMs as interpretable, efficient, and scalable handcrafted descriptors for WSIs, alongside a novel two-dimensional digital filter architecture designed to enhance numerical stability and hardware compatibility during TM computation. We conducted a comprehensive reconstruction analysis using H&E-stained WSIs from the MIDOG++ dataset to evaluate TM effectiveness. Our results demonstrate that lower-order TMs accurately reconstruct both square and rectangular WSI patches, with performance stabilising beyond a threshold moment order, confirmed by SNIRE, SSIM, and BRISQUE metrics, highlighting their capacity to retain structural fidelity. Furthermore, our analysis reveals significant computational efficiency gains through the use of pre-computed polynomials. These findings establish TMs as highly promising, interpretable, and scalable feature descriptors, offering a robust alternative for computational pathology applications that prioritise both accuracy and transparency. Full article

Hepatocellular carcinoma (HCC) and pancreatic ductal adenocarcinoma (PDAC) are aggressive malignancies characterized by a poor prognosis and resistance to conventional therapies. Mounting evidence suggests the pivotal role of epithelial–mesenchymal transition (EMT) in tumor progression, metastasis, and therapeutic resistance in these cancers. Protein induced by vitamin K absence II (PIVKA-II)—a valuable HCC detector—has ultimately emerged as a potentially relevant biomarker in PDAC, serving as both a serum biomarker and a prognostic indicator. This study investigates the putative link between PIVKA-II expression and the EMT process in HCC and PDAC. Using a Western blot analysis and electrochemiluminescence immunoassay (ECLIA), we quantified PIVKA-II serum levels alongside two canonical EMT markers—Vimentin and E-cadherin—in selected cohorts. Emerging data suggest a dual, context-dependent role for PIVKA-II. Beyond its diagnostic value in both malignancies, its co-expression with EMT markers points to a potential mechanistic involvement in tumor invasiveness and phenotypic plasticity. Notably, the selective detection of E-cadherin in HCC implies limited EMT activation and a preservation of the epithelial phenotype, whereas the higher expression of Vimentin in PDAC reflects a more substantial shift toward EMT. We provide a comprehensive analysis of key molecular markers, their involvement in EMT-driven pathophysiological mechanisms, and their potential as novel diagnostic tools. Full article

Background/Objectives: This single-cohort follow-up study describes the median overall survival (OS) in patients with unresectable head and neck squamous cell carcinoma (HNSCC) due to invasion of vital structures, which is under-represented in the current literature. Secondarily, subgroups were evaluated according to the type of presentation, in order to identify clinical characteristics and contribute to developing an appropriate treatment plan and managing patient’s expectations. Methods: This single-cohort observational study analysed the OS of 39 patients from the Otolaryngology Department with advanced-stage head and neck cancer with invasion of vital anatomical structures considered ineligible for surgical treatment. Secondarily, subgroups were evaluated according to type of presentation and various clinical characteristics. Results: A total of 39 patients radiologically classified as having unresectable HNSCC (i.e., unsuitable for surgical resection), with a mean age of 66.87 years, were included during a 24-month follow-up. By the end of the study, 56.4% of the patients had died. The median OS was 16.09 months. Statistically significant differences were observed when comparing human papilloma virus (HPV)-positive and -negative status and when comparing initial and recurrent tumours. Conclusions: The invasion of anatomical structures such as the skull base, internal carotid artery, and prevertebral space was associated with a marked decrease in survival, with an OS time of 16 months. This study provides valuable evidence in patients with unresectable HNSCC, highlighting tumour recurrence and HPV-negative status as important indicators of poor prognosis. Full article

This study assessed the seroprevalence of anti-Anisakis simplex antibodies in Norwegian patients with inflammatory bowel disease (IBD), specifically ulcerative colitis (UC) and Crohn’s disease (CD), compared with healthy controls. Associations between anti-A. simplex antibody positivity and clinical or laboratory parameters in IBD were also explored. A total of 86 UC patients, 68 CD patients, and 41 healthy controls were prospectively enrolled from four Norwegian hospitals (2013–2022). Diagnosis and disease activity were established using standard clinical, endoscopic, and biomarker criteria. Serum samples were analyzed for total Ig, IgG, IgM, IgA, and IgE antibodies against A. simplex and Pseudoterranova decipiens using ELISA. Anti-A. simplex IgG seroprevalence was 4.9% in controls and 3.2% in IBD (3.5% UC, 2.9% CD). IgM seroprevalence was 0% in all groups. IgA seroprevalence was higher in IBD (16.2%) than controls (4.9%), with 14.0% in UC and 19.1% in CD. IgE seroprevalence was low across all groups. Smoking correlated with lower antibody levels and higher surgery rates. In UC, higher anti-A. simplex IgG and IgE levels were associated with milder disease and better prognosis. Anti-TNFα and azathioprine treatments were linked to higher anti-A. simplex IgA. Norwegian UC and CD patients had significantly higher anti-A. simplex total Ig and IgA seroprevalence than healthy controls, indicating increased exposure or immune response. Anti-A. simplex IgG and IgE may serve as markers of clinical activity in UC. Further research is warranted to clarify the clinical significance of these findings. Full article

Carboxypeptidase A4 (CPA4) is an exopeptidase that cleaves peptide bonds at the C-terminal domain within peptides and proteins. It preferentially cleaves peptides with terminal aromatic or branched chain amino acid residues such as phenylalanine, tryptophan, or leucine. CPA4 was first discovered in prostate cancer cells, but it is now known to be expressed in various tissues throughout the body. Its physiologic expression is governed by latexin, a noncompetitive endogenous inhibitor of CPA4. Nevertheless, the overexpression of CPA4 has been associated with the progression and aggressiveness of many malignancies, including prostate, pancreatic, breast and lung cancer, to name a few. CPA4’s role in cancer has been attributed to its disruption of many cellular signaling pathways, e.g., PI3K-AKT-mTOR, STAT3-ERK, AKT-cMyc, GPCR, and estrogen signaling. The dysregulation of these pathways by CPA4 could be responsible for inducing epithelial--mesenchymal transition (EMT), tumor invasion and drug resistance. Although CPA4 has been found to regulate cancer aggressiveness and poor prognosis, no comprehensive review summarizing the role of CPA4 in cancer is available so far. In this review, we provide a brief description of peptidases, their classification, history of CPA4, mechanism of action of CPA4 as a peptidase, its expression in various tissues, including cancers, its role in various tumor types, the associated molecular pathways and cellular processes. We further discuss the limitations of current literature linking CPA4 to cancers and challenges that prevent using CPA4 as a biomarker for cancer aggressiveness and predicting drug response and highlight a number of future strategies that can help to overcome the limitations. Full article

Glioblastoma (GBM) is the most aggressive primary brain tumor, characterized by rapid progression, profound heterogeneity, and resistance to conventional therapies. This review provides an integrated overview of GBM’s pathophysiology, highlighting key mechanisms such as neuroinflammation, genetic alterations (e.g., EGFR, PDGFRA), the tumor microenvironment, microbiome interactions, and molecular dysregulations involving gangliosides and sphingolipids. Current diagnostic strategies, including imaging, histopathology, immunohistochemistry, and emerging liquid biopsy techniques, are explored for their role in improving early detection and monitoring. Treatment remains challenging, with standard therapies—surgery, radiotherapy, and temozolomide—offering limited survival benefits. Innovative therapies are increasingly being explored and implemented, including immune checkpoint inhibitors, CAR-T cell therapy, dendritic and peptide vaccines, and oncolytic virotherapy. Advances in nanotechnology and personalized medicine, such as individualized multimodal immunotherapy and NanoTherm therapy, are also discussed as strategies to overcome the blood–brain barrier and tumor heterogeneity. Additionally, stem cell-based approaches show promise in targeted drug delivery and immune modulation. Non-conventional strategies such as ketogenic diets and palliative care are also evaluated for their adjunctive potential. While novel therapies hold promise, GBM’s complexity demands continued interdisciplinary research to improve prognosis, treatment response, and patient quality of life. This review underscores the urgent need for personalized, multimodal strategies in combating this devastating malignancy. Full article

Pediatric high-grade glioma (pHGG) is a devastating group of childhood cancers associated with poor outcomes. Traditionally, diagnosis was based on histologic and immunohistochemical characteristics, including high mitotic activity, presence of necrosis, and presence of glial cell markers (e.g., GFAP). With advances in molecular tumor profiling, these tumors have been recategorized based on specific molecular findings that better lend themselves to prediction of treatment response and prognosis. pHGG is now categorized into four subtypes: H3K27-altered, H3G34-mutant, H3/IDH-WT, and infant-type high-grade glioma (iHGG). Molecular profiling has not only increased the specificity of diagnosis but also improved prognostication. Additionally, these molecular findings provide novel targets for individual tumor-directed therapy. While these therapies are largely still under investigation, continued investigation of distinct molecular markers in these tumors is imperative to extending event-free survival (EFS) and overall survival (OS) for patients with pHGG. Full article

Progress in establishing environmental risk factors and, consequently, prophylactic measures for glial tumors, particularly for glioblastomas, is of utmost importance, considering the dismal prognosis and limited treatment options. This report surveyed updates on established and recently identified factors that can predispose a patient to glioma formation while highlighting possible mechanistic links and further research directions. In addition to established factors that increase the risk of glioma, i.e., brain irradiation and several genetic syndromes, another group consists of likely factors contributing to such risks, such as the use of tobacco and those yielding ambiguous results (e.g., UV exposure). Oxidative stress is a common denominator for several types of exposure, and a mechanistic background for other factors remains elusive. Nevertheless, the analysis of clinical and basic research strongly suggests that, apart from the effect of environmental stressors on DNA alterations and mutation burden, the impact of modifying the tumor microenvironment should be considered. Identifying the involvement of environmental hazards in gliomagenesis and glial tumor progression would lower overall risk by modifying clinical practice, patient management, and lifestyle choices. Further verifying the environmental hazards in glioma formation and progression would have far-reaching implications for neurologists, neurosurgeons, and patients. 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: Colorectal liver metastasis (CRLM) represents a major clinical challenge in oncology, affecting 25–50% of colorectal cancer patients and significantly impacting survival. While multimodal therapies—including surgical resection, systemic chemotherapy, and local ablative techniques—have improved outcomes, prognosis remains heterogeneous due to variations in tumor biology, patient factors, and institutional practices. Methods: This review synthesizes current evidence on prognostic factors influencing CRLM management, encompassing clinical (e.g., tumor burden, anatomic distribution, timing of metastases), biological (e.g., CEA levels, inflammatory markers), and molecular (e.g., RAS/BRAF mutations, MSI status, HER2 alterations) determinants. Results: Key findings highlight the critical role of molecular profiling in guiding therapeutic decisions, with RAS/BRAF mutations predicting resistance to anti-EGFR therapies and MSI-H status indicating potential responsiveness to immunotherapy. Emerging tools like circulating tumor DNA (ctDNA) and radiomics offer promise for dynamic risk stratification and early recurrence detection, while the gut microbiome is increasingly recognized as a modulator of treatment response. Conclusions: Despite advancements, challenges persist in standardizing resectability criteria and integrating multidisciplinary approaches. Current guidelines (NCCN, ESMO, ASCO) emphasize personalized strategies but lack granularity in terms of incorporating novel biomarkers. This exhaustive review underscores the imperative for the development of a unified, biomarker-integrated framework to refine CRLM management and improve long-term outcomes. Full article

Background and Objectives: Heart failure (HF) and chronic kidney disease (CKD) frequently coexist and are closely interrelated, significantly affecting clinical outcomes. Among CKD-related markers, albuminuria and estimated glomerular filtration rate (eGFR) have emerged as key prognostic indicators in HF. However, their specific predictive value across different HF phenotypes—namely HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF)—remains incompletely understood. This systematic review aims to evaluate the prognostic significance of albuminuria and eGFR in patients with HF and to compare their predictive roles in HFpEF versus HFrEF populations. Materials and Methods: We conducted a systematic search of major databases to identify clinical studies evaluating the association between albuminuria, eGFR, and adverse outcomes in HF patients. Inclusion criteria encompassed studies reporting on cardiovascular events, all-cause mortality, or HF-related hospitalizations, with subgroup analyses based on ejection fraction. Data extraction and quality assessment were performed independently by two reviewers. Results: Twenty-one studies met the inclusion criteria, including diverse HF populations and various biomarker assessment methods. Both albuminuria and reduced eGFR were consistently associated with increased risk of mortality and hospitalization. In HFrEF populations, reduced eGFR demonstrated stronger prognostic associations, whereas albuminuria was predictive across both HF phenotypes. Heterogeneity in study design and outcome definitions limited comparability. Conclusions: Albuminuria and eGFR are valuable prognostic biomarkers in HF and may enhance risk stratification and clinical decision-making, particularly when integrated into clinical assessment models. Differential prognostic implications in HFpEF versus HFrEF highlight the need for phenotype-specific approaches. Further research is warranted to validate these findings and clarify their role in guiding personalized therapeutic strategies in HF populations. Limitations: The current evidence base consists primarily of observational studies with variable methodological quality and inconsistent reporting of effect estimates. Full article

Background: COVID-19-associated coagulopathy (CAC) is a complex condition, with high rates of thrombosis, high levels of inflammation markers and hypercoagulation (increased levels of fibrinogen and D-Dimer), as well as extensive microthrombosis in the lungs and other organs of the deceased. It resembles, without being identical, other coagulation disorders such as sepsis-DIC (SIC/DIC), hemophagocyte syndrome (HPS) and thrombotic microangiopathy (TMA). Platelets (PLTs), key regulators of thrombosis, inflammation and immunity, are considered an important risk mediator in COVID-19 pathogenesis. Platelet index MPV/PLT ratio is reported in the literature as more specific in the prognosis of platelet-related systemic thrombogenicity. Studies of MPV/PLT ratio with regards to the severity of COVID-19 disease are limited, and there are no references regarding this ratio to the outcome of COVID-19 disease at specific time points of hospitalization. The aim of this study is to evaluate the relationship of COVID-19 mortality with the red cell distribution width–coefficient of variation (RDW-CV), platelets and MPV/PLT ratio parameters. Methods: Values of these parameters in 511 COVID-19 hospitalized patients were recorded (a) on admission, (b) as mean values of the 1st and 2nd week of hospitalization, (c) over the total duration of hospitalization, (d) as nadir and zenith values, and (e) at discharge. Results: As for mortality (survivors vs. deceased), statistical analysis with ROC curves showed that regarding the values of the parameters on admission, only the RDW-CV baseline was of prognostic value. Platelet parameters, absolute number and MPV/PLT ratio had predictive potential for the disease outcome only as 2nd week values. On the contrary, with regards to disease severity (mild/moderate versus severe/critical), only the MPV/PLT ratio on admission can be used for prognosis, and to a moderate degree. On multivariable logistic regression analysis, only the RDW-CV mean hospitalization value (RDW-CV mean) was an independent and prognostic variable for mortality. Regarding disease severity, the MPV/PLT ratio on admission and RDW-CV mean were independent and prognostic variables. Conclusions: RDW-CV, platelets and MPV/PLT ratio hematological parameters could be of predictive value for mortality and severity in COVID-19 disease, depending on the hospitalization timeline. Full article

Background/Objectives: Alterations in DNA damage repair mechanisms can impair the therapeutic effectiveness of cisplatin. MicroRNAs (miRNAs), key regulators of DNA damage repair processes, have been proposed as promising biomarkers for predicting the response to platinum-based chemotherapy (CT) in non-small cell lung cancer (NSCLC). In this study, by using a bioinformatics approach, we identified six miRNAs, which were differentially expressed (DE) between NSCLC patients characterized as responders and non-responders to platinum-based CT. We further validated the differential expression of the selected miRNAs on tumor and matched normal tissues from patients with resected NSCLC. Methods: Two miRNA microarray expression datasets were retrieved from the Gene Expression Omnibus (GEO) repository, comprising a total of 69 NSCLC patients (N = 69) treated with CT and annotated data from their response to treatment. Differential expression analysis was performed using the Linear Models for Microarray Analysis (Limma) package in R to identify DE miRNAs between responders (N = 33) and non-responders (N = 36). Quantitative real-time PCR (qRT-PCR) was used to assess miRNA expression levels in clinical tissue samples (N = 20). Results: Analysis with the Limma package revealed 112 DE miRNAs between responders and non-responders. A random-effects meta-analysis further identified 24 miRNAs that were consistently up- or downregulated in at least two studies. Survival analysis using the Kaplan–Meier plotter (KM plotter) indicated that 22 of these miRNAs showed significant associations with prognosis in NSCLC. Functional and pathway enrichment analysis revealed that several of the identified miRNAs were linked to key pathways implicated in DNA damage repair, including the p53, Hippo, PI3K and TGF-β signaling pathways. We finally distinguished a six-miRNA signature consisting of miR-26a, miR-29c, miR-34a, miR-30e-5p, miR-30e-3p and miR-497, which were downregulated in non-responders and are involved in at least three DNA damage repair pathways. Comparative expression analysis on tumor and matched normal tissues from surgically treated NSCLC patients confirmed their differential expression in clinical samples. Conclusions: In summary, we identified a signature of six miRNAs that are suppressed in NSCLC and may serve as a predictor of cisplatin response in NSCLC. Full article

Background and Objectives: Euthyroid sick syndrome, or non-thyroidal illness syndrome, has been observed in severely ill patients and has been found to be an index of prognosis. It has been detected in patients with severe infectious diseases, e.g., those with severe COVID-19 infection. Prognostic indicators of the outcome of severe COVID-19 disease are important for the prognosis of individual as well as groups of patients. The aim of this study was to identify euthyroid sick syndrome in patients admitted for severe COVID-19 disease and its relationship to disease severity and outcome. Materials and Methods: In a cohort of patients admitted to hospital for severe COVID-19 disease, thyroid function in patients requiring hospitalization was evaluated by measuring TSH, FreeT₃ (FT₃), and FreeT₄ (FT₄) levels. Patients were classified into four groups: a group with uncompromised respiratory function (pO2 > 70 mmHg, without need of oxygen supplementation) (disease severity 1); a group with mild respiratory insufficiency (pO2 50–60 mmHg, in need of oxygen supplementation with nasal cannula) (disease severity 2); a group with severe respiratory insufficiency (pO2 < 50 mmHg, in need of oxygen supplementation with high flow oxygen) (disease severity 3); and a group with severe respiratory insufficiency requiring intubation (pO2 < 60 mmHg on high flow oxygen supplementation) (disease severity 4). Results: In this cohort, euthyroid sick syndrome was diagnosed in 57.1% of the patients. The presence of euthyroid sick syndrome was related to increased disease severity and adverse disease outcome, i.e., death. FT₃ levels were inversely related to CRP levels. Conclusions: Euthyroid sick syndrome may be observed in severe COVID-19 disease and is related to increased disease severity and adverse outcomes. Measurement of thyroid hormones in patients hospitalized for severe COVID-19 infection may aid in the prognosis of the disease. Full article

Genome instability is a hallmark of cancer, often driven by mutations and altered expression of genome maintenance factors involved in DNA replication and repair. Rap1 Interacting Factor 1 (RIF1) plays a crucial role in genome stability and is implicated in cancer pathogenesis. Cells express two RIF1 splice variants, RIF1-Long and RIF1-Short, which differ in their ability to protect cells from DNA replication stress. Here, we investigate differential expression and splicing of RIF1 in cancer cell lines following replication stress and in patients using matched normal and tumour data from The Cancer Genome Atlas (TCGA). Overall RIF1 expression is altered in several cancer types, with increased transcript levels in colon and lung cancers. RIF1 also exhibits distinct splicing patterns, particularly in specific breast cancer subtypes. In Luminal A (LumA), Luminal B (LumB), and HER2-enriched breast cancers (HER2E), RIF1 Exon 31 tends to be excluded, favouring RIF1-Short expression and correlating with poorer clinical outcomes. These breast cancer subtypes also tend to exclude other short exons, suggesting length-dependent splicing dysregulation. Basal breast cancer also shows exon exclusion, but unlike other subtypes, it shows no short-exon bias. Surprisingly, however, in basal breast cancer, RIF1 Exon 31 is not consistently excluded, which may impact prognosis since RIF1-Long protects against replication stress. Full article