Search Results (5,235)

Noninvasive liquid biopsy for monitoring circulating tumor cells offers valuable insights for predicting therapeutic responses. We developed TelomeScan^® (OBP-401), based on the detection of telomerase activity as a universal cancer cell marker and an indicator of the presence of viable circulating tumor cells (CTCs) for patients with advanced non-small cell lung cancer (NSCLC). This system evaluated CTC subtypes characterized by programmed death ligand 1 (PD-L1), an immune checkpoint molecule, and vimentin, an epithelial–mesenchymal transition (EMT) marker, using a multi-fluorescent color microscope reader. The prognostic value and therapeutic responses were predicted by dynamically monitoring CTC counts in 79 patients with advanced NSCLC. The sensitivity and specificity values of TelomeScan^® for PD-L1⁽⁺⁾ cells (≥1 cell) were 75% and 100%, respectively, indicating high diagnostic accuracy. PD-L1⁽⁺⁾ and EMT⁽⁺⁾ in CTCs were detected in 75% and 12% of patients, respectively. Detection of PD-L1⁽⁺⁾CTCs and PD-L1⁽⁺⁾EMT⁽⁺⁾ CTCs before treatment was associated with poor prognosis (p < 0.05). Monitoring of reducing and increasing PD-L1⁽⁺⁾ CTC counts in two sequential samples (baseline, cycle 2 treatment) correlated significantly with partial response (p = 0.032) and progressive disease (p = 0.023), respectively. Monitoring PD-L1⁽⁺⁾CTCs by TelomeScan^® will aid in anticipating responses or resistance to frontline treatments, optimizing precision medicine choices in patients with NSCLC. Full article

Background: Stomach adenocarcinoma is a major contributor to worldwide mortality and significantly impacts life expectancy. The main objective of the current study was to identify a prognostic biomarker for stomach adenocarcinoma to advance translational medicine and improve patient outcomes. Method: various databases (GEPIA, UALCAN, miRNet, StarBase, and Kaplan Meier plotter) bioinformatics tools (cytoscape) and were used in this study. Results: Ten novel unfavorable prognosis-associated genes were identified. In addition, 41 potential miRNAs were predicted. ELAVL3-hsa-mir-29a-3p and CALCR-hsa-mir-29a-3p were identified as the two critical networks in the oncogenesis of stomach adenocarcinoma via bioinformatics analysis. Subsequently, the binding of lncRNAs to hsa-mir-29a-3p was predicted utilizing the starBase and miRNet databases. Following the execution of both expression and survival analyses for the predicted lncRNAs, it was determined that only one lncRNA, KCNQ1OT1, exhibited significant overexpression in stomach adenocarcinoma, and its elevated expression was associated with an unfavorable prognosis. Subsequently, we constructed a triple ceRNA network involving mRNA, miRNA, and lncRNA, which is associated with the prognosis of stomach adenocarcinoma. Conclusions: In summary, the current study provides an extensive ceRNA network that highlights novel prognostic biomarkers for stomach adenocarcinoma. Full article

Traumatic brain injury (TBI) remains a major global health problem, contributing significantly to morbidity and mortality worldwide. Despite advances in understanding its complex pathophysiology, current therapeutic strategies are insufficient in addressing the long-term cognitive, emotional, and neurological impairments. While the primary mechanical injury is immediate and unavoidable, the secondary phase involves a cascade of biological processes leading to neuroinflammation, blood–brain barrier (BBB) disruption, and systemic immune activation. The heterogeneity of patient responses underscores the urgent need for reliable biomarkers and targeted interventions. Emerging evidence highlights the gut–brain axis as a critical modulator of the secondary phase, with microbiota-derived extracellular vesicles (MEVs) representing a promising avenue for both diagnosis and therapy. MEVs can cross the intestinal barrier and BBB, carrying biomolecules that influence neuronal survival, synaptic plasticity, and inflammatory signaling. These properties make MEVs promising biomarkers for early detection, severity classification, and prognosis in TBI, while also offering therapeutic potential through modulation of neuroinflammation and promotion of neural repair. MEV-based strategies could enable tailored interventions based on the individual’s microbiome profile, immune status, and injury characteristics. The integration of multi-omics with artificial intelligence is expected to fully unlock the diagnostic and therapeutic potential of MEVs. These approaches can identify molecular subtypes, predict outcomes, and facilitate real-time clinical decision-making. By bridging microbiology, neuroscience, and precision medicine, MEVs hold transformative potential to advance TBI diagnosis, monitoring, and treatment. This review also identifies key research gaps and proposes future directions for MEVs in precision diagnostics and gut microbiota-based therapeutics in neurotrauma care. 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

Background: Hepatocellular carcinoma (HCC) is a highly aggressive and heterogeneous malignancy which predominantly arises in the setting of cirrhosis, and there is lack of models to predict prognosis in cirrhotic HCC. This study aims to develop and validate a prediction model based on the pathomics signature and clinicopathological characteristics to predict the prognosis of HCC with cirrhosis. Methods: In this multicenter, retrospective study, 389 patients were enrolled (training cohort: 268; independent validation cohort: 121). A total of 351 pathomics features were extracted from digital H-E–stained images, and a pathomics signature (PS_HCC) was constructed using a least absolute shrinkage and selection operator Cox regression model. Then two nomograms were established by combining the PS_HCC and clinicopathological characteristics. Further validation was performed in the validation cohort. Results: This study included 389 patients. A 24 feature-based PS_HCC was constructed. A higher PS_HCC was significantly associated with worse OS and DFS in both the training (OS: hazard ratio [HR], 4.341 [95% CI, 3.109–6.062]; DFS: HR, 3.058 [95% CI, 2.223–4.207]) and validation (OS: HR, 4.145 [95% CI, 2.357–7.291]; DFS: HR, 3.395 [95% CI, 2.104–5.479]) cohorts (p < 0.001 for all comparisons). Multivariable analysis revealed that the PS_HCC was an independent factor associated with OS and DFS. Integrating the PS_HCC into pathomics nomograms resulted in better performance for prognosis prediction than the traditional model in both cohorts. Conclusions: The PS_HCC may serve as a reliable surrogate for prognosis, and the nomograms offer promising tools to predict individual outcomes, facilitating personalized management of HCC with cirrhosis. Full article

Background: Wide application of genome sequencing technologies has highlighted extensive genetic diversity in Acute Myeloid Leukemia (AML), yet the specific roles of individual genes remain unclear. This systematic review and meta-analysis aims to provide robust evidence for the prognostic impact of somatic gene mutations in de novo AML patients, while also exploring the prevalence of these mutations. Methods: Eligible studies were identified from PubMed and Scopus, with a focus on those reporting the prognostic influence of somatic gene mutations on overall survival (OS) or relapse-free survival (RFS) when compared to wild-type carriers. We calculated the pooled prevalence with 95% confidence intervals to assess the frequency of these mutations, and the pooled Hazard Ratio (HR) to compare OS and RFS associated with specific gene mutations. Results: We evaluated 53 somatic gene mutations using 80 studies, involving 20,048 de novo AML patients. The analysis revealed that the most prevalent affected genes were NPM1 (27%), DNMT3A (26%), and FLT3-ITD (24%). Mutations in CSF3R, TET2, and TP53 were significantly associated with poorer OS or RFS (p < 0.05). Sensitivity analysis confirmed that ASXL1, DNMT3A, and RUNX1 mutations were consistently linked to inferior OS or RFS. In contrast, CEBPAdm mutations were associated with favorable OS [HR = 0.39 (0.30–0.50)] and RFS [HR = 0.44 (0.37–0.54)]. Subgroup analysis showed that FLT3-ITD mutations were consistently associated with worse OS or RFS across all subgroups, though no significant subgroup differences were noted. No significant impact on OS or RFS was observed for mutations in GATA2, FLT3-TKD, KRAS, NRAS, IDH1, and IDH2. Conclusions: These findings provide critical insights into AML prognosis, aiding clinical decision-making and improving risk stratification strategies. Full article

Background and Objectives: Alpha-1 antitrypsin deficiency (AATD) is a rare genetic condition associated with chronic respiratory diseases such as chronic obstructive pulmonary disease (COPD) and emphysema, and with liver involvement through a distinct toxic gain-of-function mechanism. Despite its clinical relevance, AATD remains underdiagnosed and exhibits marked phenotypic heterogeneity. Artificial intelligence (AI) has shown growing potential in respiratory medicine, yet its application to AATD is still limited. This systematic review synthesizes the clinical evidence on AI in AATD, primarily in the respiratory domain and, where available, in hepatic outcomes. Materials and Methods: We conducted a PRISMA-guided search (PubMed, Web of Science, IEEE Xplore) for original, peer-reviewed articles (January 2014–September 2025) applying AI to detection, classification, stratification, or prediction tasks in AATD. Results: Six studies met eligibility criteria. Supervised models (e.g., XGBoost, penalized regression, Transformer-based architectures) and one unsupervised approach were identified. Applications included screening in COPD populations, prediction of emphysema progression from CT, proteomic modeling of lung function, identification of clinical subgroups, and prediction of clinical outcomes in AATD-associated liver disease. External validation and genotype diversity remained limited across studies. Conclusions: Although AI shows promise in improving detection, prognosis, and patient stratification in AATD across both respiratory and hepatic manifestations, the current evidence remains limited. Broader, multicenter validation in genotype-diverse cohorts is required to confirm its clinical utility and support the implementation of precision medicine in AATD. Full article

Emerging evidence highlights the critical role of phagocytosis-related genes in CRC progression, underscoring the need for novel phagocytosis-based prognostic models to predict clinical outcomes. In this study, a four-gene (SPHK1, VSIG4, FCGR2B and FPR2) signature associated with CRC prognosis was developed using single-sample gene set enrichment analysis (ssGSEA), least absolute shrinkage and selection operator (LASSO) regression, and univariate Cox analysis. Pathway enrichment analysis was conducted on the prognostic genes, along with evaluations of the tumor microenvironment and sensitivity to immunotherapy and chemotherapy across the high- and low-risk groups. Prognostic gene validation was performed via quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) using CRC cDNA and tissue microarrays. High-risk patients showed enhanced responsiveness to immunotherapy, while chemotherapy sensitivity varied across risk subgroups. qRT-PCR results revealed upregulation of SPHK1 and FPR2 in cancer tissues, whereas FCGR2B and VSIG4 were downregulated. IHC assays confirmed increased SPHK1 and FPR2 expression in cancer samples. Single-cell RNA sequencing analysis demonstrated a decrease in SPHK1 and FCGR2B, while VSIG4 and FPR2 progressively increased during macrophage differentiation. These findings provide a potential framework for targeted therapy. Full article

Tricuspid valve diseases are an increasing cause of cardiovascular mortality, peaking in the eighth decade of life. More than 75% of severe tricuspid regurgitations are recognized via functional mechanisms, often secondary to left heart disease and pulmonary hypertension. Surgical risk for isolated correction of tricuspid regurgitation, both repair or replacement, is associated with prohibitive risk mainly in elderly patients, with several comorbidities and right ventricular dysfunction. In the past decade, different percutaneous devices have been developed to treat a large group of high-surgical-risk patients. Early diagnosis and careful patient selection are essential to improving prognosis in severe TR. Potential treatment options may vary in different stages of disease. The current available results from present studies have proven the safety and effectiveness of these devices under proper clinical indications, although selection bias and non-randomization in most investigations at present do not allow for definite indications. Ideal anatomic and clinical parameters to predict interventional success are in continuous evolution and need definite standardization. We report three cases in which different percutaneous techniques were employed for treatment when surgery was not suitable. The literature is discussed for each condition. Despite promising results in terms of safety and success rate, further randomized studies are needed to better understand which patients may be subject to long-term effects on survival and quality of life. Full article

Background/Objectives: Electronic fetal monitoring (EFM) has been used for intrapartum fetal surveillance for over 50 years. Despite numerous trials comparing EFM with standard fetal heart rate (FHR) auscultation, it remains contentious whether continuous monitoring with standard interpretation has reliably improved perinatal outcomes, specifically lower rates of perinatal morbidity and mortality. This review examines previous attempts to improve fetal monitoring and presents future directions for novel intrapartum fetal surveillance systems. Methods: We conducted a chronological review of EFM developments, including ancillary methods such as fetal ECG analysis, automated systems for FHR analysis, and artificial intelligence applications. We analyzed the evolution from visual interpretation to intelligent systems and evaluated the performance of various automated monitoring platforms. Results: Various ancillary methods developed to improve EFM accuracy for predicting fetal compromise have shown limited success. Only a limited number of studies demonstrated that adding fetal ECG analysis to visual FHR pattern interpretation resulted in better fetal outcomes. Automated systems for FHR analysis have not consistently enhanced intrapartum fetal surveillance. However, novel approaches such as the Fetal Reserve Index (FRI) show promise by incorporating clinical risk factors with traditional FHR patterns to provide higher-level risk assessment and prognosis. Conclusions: The shortcomings of visual interpretation of FHR patterns persist despite technological advances. Future intelligent intrapartum surveillance systems must combine conventional fetal monitoring with comprehensive risk assessment that incorporates maternal, fetal, and obstetric factors. The integration of artificial intelligence with contextualized metrics like the FRI represents the most promising direction for improving intrapartum fetal surveillance and clinical outcomes. Full article

Background: While hyperuricemia has been widely studied in cardiovascular and renal diseases, the prognostic impact of low serum uric acid (UA) remains unclear. Emerging evidence suggests hypouricemia may be linked to increased mortality and adverse outcomes. This study aimed to assess the relationship between low UA levels and poor outcomes, including mortality and intensive care unit (ICU) admission, in hospitalized patients. Methods: This retrospective cohort study included 1679 hospitalized patients (744 females, 935 males) from the Internal Medicine Clinic. Patients were categorized into normal and low UA groups based on sex-specific thresholds (male: <3.4 mg/dL, female: <2.4 mg/dL). The primary outcome was in-hospital mortality; secondary outcomes were ICU admission and discharge status. Logistic regression models adjusted for age, chronic kidney disease (CKD), hypertension (HT), and coronary artery disease (CAD). A Prognostic Uric Acid Score (PUAS) was developed using significant predictors and evaluated by Receiver Operating Characteristic (ROC) analysis. Results: Low UA levels were significantly associated with higher ICU admission and mortality (p = 0.012). Multivariate analysis identified age (OR: 1.032), low UA (OR: 2.285), and CKD (OR: 1.571) as predictors of poor prognosis. PUAS showed moderate performance (AUC: 0.664), with a cutoff score of 3.5 optimizing sensitivity and specificity. Conclusions: Low UA levels independently predict mortality and poor prognosis in hospitalized patients. These findings support routine UA monitoring and suggest hypouricemia may be a useful prognostic biomarker. Further studies are needed to understand clinical implications and guide UA-targeted interventions. Full article

Personalised modelling has become dominant in personalised medicine and precision health. It creates a computational model for an individual based on large data repositories of existing personalised data, aiming to achieve the best possible personal diagnosis or prognosis and derive an informative explanation for it. Current methods are still working on a single data modality or treating all modalities with the same method. The proposed method, SAIN (Search-And-INfer), offers better results and an informative explanation for classification and prediction tasks on a new multimodal object (sample) using a database of similar multimodal objects. The method is based on different distance measures suitable for each data modality and introduces a new formula to aggregate all modalities into a single vector distance measure to find the closest objects to a new one, and then use them for a probabilistic inference. This paper describes SAIN and applies it to two types of multimodal data, cardiovascular diagnosis and EEG time series, modelled by integrating modalities, such as numbers, categories, images, and time series, and using a software implementation of SAIN. Full article

Background: Cancer staging, guided by anatomical and clinicopathologic factors, is essential for determining treatment strategies and patient prognosis. The current gold standard for prostate cancer is the American Joint Committee on Cancer (AJCC) Tumor, Lymph Node, and Metastasis (TNM) Staging System 9th Version (2024). This system incorporates five prognostic variables: tumor (T), spread to lymph nodes (N), metastasis (M), prostate-specific antigen (PSA) levels (P), and Grade Group/Gleason score (G). While effective, further refinement of prognostic systems may improve prediction of patient outcomes and support more individualized treatment. Methods: We applied the Ensemble Algorithm for Clustering Cancer Data (EACCD), an unsupervised machine learning approach. EACCD involves three steps: calculating initial dissimilarities, performing ensemble learning, and conducting hierarchical clustering. We first developed an EACCD model using the five AJCC variables (T, N, M, P, G). The model was then expanded to include two additional factors, age (A) and race (R). Prostate cancer patient data were obtained from the Surveillance, Epidemiology, and End Results (SEER) program from the National Cancer Institute. Results: The EACCD algorithm effectively stratified patients into distinct prognostic groups, each with well-separated survival curves. The five-variable model achieved a concordance index (C-index) of 0.8293 (95% CI: 0.8245–0.8341), while the seven-variable model, including age and race, improved performance to 0.8504 (95% CI: 0.8461–0.8547). Both outperformed the AJCC TNM system, which had a C-index of 0.7676 (95% CI: 0.7622–0.7731). Conclusions: EACCD provides a refined prognostic framework for primary localized prostate cancer, demonstrating superior accuracy over the AJCC staging system. With further validation in independent cohorts, EACCD could enhance risk stratification and support precision oncology. Full article

Background/Objectives: Radiomics is a powerful and emerging tool in oncology, with many potential applications in predicting therapy response and prognosis. To assess the current state of radiomics in melanoma, we conducted a systematic review of its various clinical uses. Methods: We searched three databases: PubMed, Web of Science and Scopus. Each study was classified based on multiple variables, including patient number, metastasis number, therapy, imaging modality, clinical endpoints and analysis methods. The risk of bias in the systematic review was assessed with QUADAS-2, and the certainty of evidence in the meta-analysis with GRADE. Results: Forty studies involving 4673 patients and 24,561 lesions were included in the analysis. Metastatic disease was the most frequently studied clinical setting (85%). Immunotherapy was the most commonly investigated treatment, featured in half of the studies. Computed tomography (CT) was the preferred imaging modality, appearing in 17 studies (42.5%). Radiomic features were most often extracted using three-dimensional (3D) analysis (72.5%). Across 24 studies investigating the prediction of treatment response and survival, only 9 provided sufficient data (Area Under the Curve, AUC, and standard error, SE) for inclusion. A random-effects model estimated a pooled AUC of 0.83 (95% CI: 0.74 to 0.92), indicating strong discriminative performance of the radiomic models included. Low to moderate heterogeneity was observed (I² = 28.6%, p = 0.4741). No evidence of publication bias was detected (p = 0.470). Conclusions: Radiomics is increasingly being explored in the context of melanoma, particularly in advanced disease settings and in relation to immunotherapy. Most studies rely on CT imaging and 3D feature extraction, while molecular integration remains limited. Despite promising findings with strong discriminative performance in predicting therapy response, further prospective, standardized studies with higher methodological rigor are needed to validate radiomic biomarkers and integrate them into clinical decision-making. Full article

Background/Objective: Malnutrition and sarcopenia are common complications after ischemic stroke and have a negative impact on prognosis. The C-reactive protein/albumin ratio (CAR) reflects both inflammation and nutritional status, but its predictive role in this setting has not been widely studied. This study aimed to investigate the predictive value of CAR (C-reactive protein/albumin ratio) for malnutrition risk and probable sarcopenia in patients with ischemic stroke. Methods: In this prospective observational study, 197 patients with acute ischemic stroke were evaluated. Patients with chronic renal or hepatic failure, malignancy, active infection, and hand disability preventing grip strength measurement were excluded. Demographic data (age, sex), vascular risk factors, the NIHSS score, and laboratory parameters were recorded. The nutritional status of patients was assessed using the Nutritional Risk Screening-2002 (NRS-2002), and sarcopenia risk was evaluated with the SARC-F questionnaire. Handgrip strength was measured in patients with high SARC-F scores to define probable sarcopenia. CAR was calculated from serum CRP and albumin levels. Logistic regression was applied to identify independent predictors, and receiver operating characteristic (ROC) analyses were performed to determine the discriminatory ability and cut-off values of CAR. The nutritional status of patients admitted to the neurology clinic with acute ischemic stroke was assessed using the Nutritional Risk Screening-2002 (NRS-2002), and sarcopenia risk was evaluated with the SARC-F questionnaire. Handgrip strength was measured in patients with high SARC-F scores to define probable sarcopenia. CAR was calculated from serum CRP and albumin levels. Logistic regression and receiver operating characteristic (ROC) analyses were performed. Results: Malnutrition risk was identified in 32.5% of patients, and probable sarcopenia was identified in 19.3% of patients. ROC analysis showed that CAR had acceptable discriminatory power for both conditions. In multivariate analysis, CAR was consistently identified as an independent predictor of malnutrition risk and possible sarcopenia. ROC analysis for malnutrition risk showed an AUC of 0.750 (cut-off: 0.306; sensitivity 68.8%; specificity 75.2%). In regression analysis, CAR (OR = 2.13; 95% CI: 1.39–3.26; p < 0.001), age (OR = 1.05; 95% CI: 1.02–1.09; p = 0.003), and NIHSS (OR = 1.11; 95% CI: 1.01–1.23; p = 0.026) were independent predictors. For probable sarcopenia, ROC analysis revealed an AUC of 0.814 (cut-off: 0.320; sensitivity 81.6%; specificity 71.7%). Multivariate analysis identified CAR (OR = 1.73; 95% CI: 1.19–2.52; p = 0.004), age (OR = 1.11; 95% CI: 1.05–1.18; p < 0.001), and NIHSS (OR = 1.19; 95% CI: 1.05–1.35; p = 0.007) as independent predictors. Conclusions: CAR was identified as an independent predictor of both malnutrition risk and probable sarcopenia in ischemic stroke patients. CAR may serve as a reliable biomarker for early nutritional and functional risk stratification in clinical practice. Full article