BACKGROUND: Urinary bladder neoplasms constitute a heterogeneous group of tumors with diverse clinical behaviors and outcomes. Understanding the correlation between clinicopathological characteristics and the prognostic significance of molecular biomarkers in bladder cancer is vital for personalized treatment strategies and improved patient outcomes.
OBJECTIVE: This prospective observational study aimed to comprehensively investigate the clinicopathological correlations and prognostic significance of molecular biomarkers in urinary bladder neoplasms.
METHODS: A cohort of 174 patients diagnosed with urinary bladder neoplasm participated in this study. Clinicopathological data, including demographic information, medical history, imaging findings, and histopathological reports, were collected from the patient records. Tissue samples obtained from transurethral resection or biopsy were subjected to molecular biomarker analysis using immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), and molecular profiling techniques. Longitudinal follow-up assessments were conducted to monitor disease progression, recurrence, and overall survival.
RESULTS: Out of 174 patients diagnosed with bladder neoplasms, the mean age of the patients was 62.4 years (±8.7), indicating that the study cohort primarily comprised elderly individuals. The majority of patients were male (126, 72.4%), reflecting the higher prevalence of bladder cancer among men compared to women. Preliminary analysis revealed significant associations between clinicopathological parameters, molecular biomarker expression profiles, and clinical outcomes in patients with urinary bladder neoplasms. Elevated expression levels of specific biomarkers such as tumor protein p53 (p53), Ki-67, and estimated glomerular filtration rate (EGFR) were observed in advanced tumor stages (p < 0.001) and higher histological grades (p < 0.05), indicating their potential prognostic significance. Furthermore, genetic alterations detected using molecular profiling techniques, including chromosomal gains and losses, were significantly correlated with aggressive disease phenotypes and increased recurrence risk (p < 0.01). Longitudinal follow-up data demonstrated that patients with elevated biomarker expression levels or genetic alterations had poorer treatment responses and shorter overall survival durations than those with lower biomarker expression levels.
CONCLUSIONS: This study highlights the importance of integrating clinicopathological parameters and molecular biomarker data for the risk stratification, treatment selection, and prognostic assessment of urinary bladder neoplasms.

Neurological involvement has been widely reported in SARS-CoV-2 infection. However, viral identification in the cerebrospinal fluid (CSF) is rarely found. The aim of this study is to evaluate the accuracy of virological and immunological biomarkers in CSF for the diagnosis of neuroCOVID-19. We analyzed 69 CSF samples from patients with neurological manifestations: 14 with suspected/confirmed COVID-19, with 5 additional serial CSF samples (group A), and as a control, 50 non-COVID-19 cases (group B-26 with other neuroinflammatory diseases; group C-24 with non-inflammatory diseases). Real-time reverse-transcription polymerase chain reaction (real-time RT-PCR) was used to determine SARS-CoV-2, and specific IgG, IgM, neopterin, and protein 10 induced by gamma interferon (CXCL-10) were evaluated in the CSF samples. No samples were amplified for SARS-CoV-2 by real-time RT-PCR. The sensitivity levels of anti-SARS-CoV-2 IgG and IgM were 50% and 14.28%, respectively, with 100% specificity for both tests. CXCL-10 showed high sensitivity (95.83%) and specificity (95.83%) for detection of neuroinflammation. Serial CSF analysis showed an association between the neuroinflammatory biomarkers and outcome (death and hospital discharge) in two cases (meningoencephalitis and rhombencephalitis). The detection of SARS-CoV-2 RNA and specific immunoglobulins in the CSF can be used for neuroCOVID-19 confirmation. Additionally, CXCL-10 in the CSF may contribute to the diagnosis and monitoring of neuroCOVID-19.

UNASSIGNED: Molecular biomarkers are reshaping patient stratification and treatment decisions, yet their precise use and best implementation remain uncertain. Intratumor heterogeneity (ITH), an area of increasing research interest with prognostic value across various conditions, lacks defined clinical relevance in certain non-small cell lung cancer (NSCLC) subtypes. Exploring the relationship between ITH and tumor mutational burden (TMB) is crucial, as their interplay might reveal distinct patient subgroups. This study evaluates how the ITH-TMB dynamic affects prognosis across the two main histological subtypes of NSCLC, squamous cell and adenocarcinoma, with a specific focus on early-stage cases to address their highly unmet clinical needs.
UNASSIGNED: We stratify a cohort of 741 early-stage NSCLC patients from The Cancer Genome Atlas (TCGA) based on ITH and TMB and evaluate differences in clinical outcomes. Additionally, we compare driver mutations and the tumor microenvironment (TME) between high and low ITH groups.
UNASSIGNED: In lung squamous cell carcinoma (LUSC), high ITH predicts an extended progression-free survival (PFS) (median: 21 vs. 14 months, P=0.01), while in lung adenocarcinoma (LUAD), high ITH predicts a reduced PFS (median: 15 vs. 20 months, P=0.04). This relationship is driven by the low TMB subset of patients. Additionally, we found that CD8 T cells were enriched in better-performing subgroups, regardless of histologic subtype or ITH status.
UNASSIGNED: There are significant differences in clinical outcomes, driver mutations, and the TME between high and low ITH groups among early-stage NSCLC patients. These differences may have treatment implications, necessitating further validation in other NSCLC datasets.

OBJECTIVE: Metabolic risk factors and plasma biomarkers for diabetes have previously been shown to change prior to a clinical diabetes diagnosis. However, these markers only cover a small subset of molecular biomarkers linked to the disease. In this study, we aimed to profile a more comprehensive set of molecular biomarkers and explore their temporal association with incident diabetes.
METHODS: We performed a targeted analysis of 54 proteins and 171 metabolites and lipoprotein particles measured in three sequential samples spanning up to 11 years of follow-up in 324 individuals with incident diabetes and 359 individuals without diabetes in the Danish Blood Donor Study (DBDS) matched for sex and birth year distribution. We used linear mixed-effects models to identify temporal changes before a diabetes diagnosis, either for any incident diabetes diagnosis or for type 1 and type 2 diabetes mellitus diagnoses specifically. We further performed linear and non-linear feature selection, adding 28 polygenic risk scores to the biomarker pool. We tested the time-to-event prediction gain of the biomarkers with the highest variable importance, compared with selected clinical covariates and plasma glucose.
RESULTS: We identified two proteins and 16 metabolites and lipoprotein particles whose levels changed temporally before diabetes diagnosis and for which the estimated marginal means were significant after FDR adjustment. Sixteen of these have not previously been described. Additionally, 75 biomarkers were consistently higher or lower in the years before a diabetes diagnosis. We identified a single temporal biomarker for type 1 diabetes, IL-17A/F, a cytokine that is associated with multiple other autoimmune diseases. Inclusion of 12 biomarkers improved the 10-year prediction of a diabetes diagnosis (i.e. the area under the receiver operating curve increased from 0.79 to 0.84), compared with clinical information and plasma glucose alone.
CONCLUSIONS: Systemic molecular changes manifest in plasma several years before a diabetes diagnosis. A particular subset of biomarkers shows distinct, time-dependent patterns, offering potential as predictive markers for diabetes onset. Notably, these biomarkers show shared and distinct patterns between type 1 diabetes and type 2 diabetes. After independent replication, our findings may be used to develop new clinical prediction models.

Accurate prediction and grading of gliomas play a crucial role in evaluating brain tumor progression, assessing overall prognosis, and treatment planning. In addition to neuroimaging techniques, identifying molecular biomarkers that can guide the diagnosis, prognosis and prediction of the response to therapy has aroused the interest of researchers in their use together with machine learning and deep learning models. Most of the research in this field has been model-centric, meaning it has been based on finding better performing algorithms. However, in practice, improving data quality can result in a better model. This study investigates a data-centric machine learning approach to determine their potential benefits in predicting glioma grades. We report six performance metrics to provide a complete picture of model performance. Experimental results indicate that standardization and oversizing the minority class increase the prediction performance of four popular machine learning models and two classifier ensembles applied on a low-imbalanced data set consisting of clinical factors and molecular biomarkers. The experiments also show that the two classifier ensembles significantly outperform three of the four standard prediction models. Furthermore, we conduct a comprehensive descriptive analysis of the glioma data set to identify relevant statistical characteristics and discover the most informative attributes using four feature ranking algorithms.

Skin cancer is the most common cancer type in the USA, with over five million annually treated cases and one in five Americans predicted to develop the disease by the age of 70. Skin cancer can be classified as melanoma or non-melanoma (NMSC), the latter including basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (SCC). Development of BCC and SCC is impacted by environmental, behavioral, and genetic risk factors and the incidence is on the rise, with the associated number of deaths surpassing those caused by melanoma, according to recent reports. Substantial morbidity is related to both BCC and SCC, including disfigurement, loss of function, and chronic pain, driving high treatment costs, and representing a heavy financial burden to patients and healthcare systems worldwide. Clinical presentations of BCC and SCC can be diverse, sometimes carrying considerable phenotypic similarities to benign lesions, and underscoring the need for the development of disease-specific biomarkers. Skin biomarker profiling plays an important role in deeper disease understanding, as well as in guiding clinical diagnosis and patient management, prompting the use of both invasive and non-invasive tools to evaluate specific biomarkers. In this work, we review the known and emerging biomarkers of BCC and SCC, with a focus on molecular and histologic biomarkers relevant for aspects of patient management, including prevention/risk assessments, tumor diagnosis, and therapy selection.

Breast cancer is a heterogeneous disease with various morphologies and molecular features, and it is the second leading cause of cancer death in women in developed countries. According to the literature, we currently lack both prognostic biomarkers and therapeutic targets. The most important prognostic factors are disease stage and Nottingham grade. We conducted a retrospective analysis involving 273 patients with BC who underwent neoadjuvant therapy before proceeding to curative surgical treatment between 1 January 2014 and 31 December 2023. Pathological procedures were conducted at the Department of Pathology, Emergency County Hospital of Targu Mureș, Romania. A statistical analysis was performed. Regarding the relationship between Nottingham grade and Ki67, grade I was associated with a Ki67 of less than 14. The relationship between tumor grade and luminal was similar (p = 0.0001): Grade I was associated with luminal A. Regarding TNM stage, it was statistically significantly correlated with TILs (p = 0.01) and RCB (p = 0.0001). Stages III and IV were associated with a high RCB and poor prognosis. Regarding the prognostic value, Nottingham grade 3 and TNM stages III and IV were correlated with low overall survival and disease-free survival, with poor prognosis, and, among the molecular variables, RCB played the most important prognostic role.

Chronic obstructive pulmonary disease (COPD) plays a significant role in global morbidity and mortality rates, typified by progressive airflow restriction and lingering respiratory symptoms. Recent explorations in molecular biology have illuminated the complex mechanisms underpinning COPD pathogenesis, providing critical insights into disease progression, exacerbations, and potential therapeutic interventions. This review delivers a thorough examination of the latest progress in molecular research related to COPD, involving fundamental molecular pathways, biomarkers, therapeutic targets, and cutting-edge technologies. Key areas of focus include the roles of inflammation, oxidative stress, and protease-antiprotease imbalances, alongside genetic and epigenetic factors contributing to COPD susceptibility and heterogeneity. Additionally, advancements in omics technologies-such as genomics, transcriptomics, proteomics, and metabolomics-offer new avenues for comprehensive molecular profiling, aiding in the discovery of novel biomarkers and therapeutic targets. Comprehending the molecular foundation of COPD carries substantial potential for the creation of tailored treatment strategies and the enhancement of patient outcomes. By integrating molecular insights into clinical practice, there is a promising pathway towards personalized medicine approaches that can improve the diagnosis, treatment, and overall management of COPD, ultimately reducing its global burden.

OBJECTIVE: To identify new biomarkers to detect untreated and treated periodontitis in gingival crevicular fluid (GCF) using sequential window acquisition of all theoretical mass spectra (SWATH-MS).
METHODS: GCF samples were collected from 44 periodontally healthy subjects and 40 with periodontitis (Stages III-IV). In the latter, 25 improved clinically 2 months after treatment. Samples were analysed using SWATH-MS, and proteins were identified by the UniProt human-specific database. The diagnostic capability of the proteins was determined with generalized additive models to distinguish the three clinical conditions.
RESULTS: In the untreated periodontitis vs. periodontal health modelling, five proteins showed excellent or good bias-corrected (bc)-sensitivity/bc-specificity values of >80%. These were GAPDH, ZG16B, carbonic anhydrase 1, plasma protease inhibitor C1 and haemoglobin subunit beta. GAPDH with MMP-9, MMP-8, zinc-α-2-glycoprotein and neutrophil gelatinase-associated lipocalin and ZG16B with cornulin provided increased bc-sensitivity/bc-specificity of >95%. For distinguishing treated periodontitis vs. periodontal health, most of these proteins and their combinations revealed a predictive ability similar to previous modelling. No model obtained relevant results to differentiate between periodontitis conditions.
CONCLUSIONS: New single and dual GCF protein biomarkers showed outstanding results in discriminating untreated and treated periodontitis from periodontal health. Periodontitis conditions were indistinguishable. Future research must validate these findings.

Alzheimer\'s Disease (AD) is a challenging neurodegenerative condition, with overwhelming implications for affected individuals and healthcare systems worldwide. Animal models have played a crucial role in studying AD pathogenesis and testing therapeutic interventions. Remarkably, studies on the genetic factors affecting AD risk, such as APOE and TREM2, have provided valuable insights into disease mechanisms. Early diagnosis has emerged as a crucial factor in effective AD management, as demonstrated by clinical studies emphasizing the benefits of initiating treatment at early stages. Novel diagnostic technologies, including RNA sequencing of microglia, offer promising avenues for early detection and monitoring of AD progression. Therapeutic strategies remain to evolve, with a focus on targeting amyloid beta (Aβ) and tau pathology. Advances in animal models, such as APP-KI mice, and the advancement of anti-Aβ drugs signify progress towards more effective treatments. Therapeutically, the focus has shifted towards intricate approaches targeting multiple pathological pathways simultaneously. Strategies aimed at reducing Aβ plaque accumulation, inhibiting tau hyperphosphorylation, and modulating neuroinflammation are actively being explored, both in preclinical models and clinical trials. While challenges continue in developing validated animal models and translating preclinical findings to clinical success, the continuing efforts in understanding AD at molecular, cellular, and clinical levels offer hope for improved management and eventual prevention of this devastating disease.