Bosnian Journal of Basic Medical Sciences (BJBMS)
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Pre-analytical storage effects on ALU- and LINE1-derived cell-free DNA biomarkers in whole blood and plasma
Full text linkCell-free DNA (cfDNA) biomarkers derived from Arthrobacter luteus (ALU) repeats and long interspersed nuclear elements 1 (LINE1) — including ALU-115, ALU-247, LINE1-97, and LINE1-266 concentrations, as well as the integrity ratios ALU-247/115 and LINE1-266/97 — are commonly utilized to assess cfDNA quantity and integrity. This study examined the impact of delayed blood processing and prolonged plasma storage on these biomarkers using quantitative polymerase chain reaction. Blood samples were collected from twelve healthy individuals (6 males; mean age, 65.8 ± 4.69 years) into dipotassium ethylenediaminetetraacetic acid tubes. Plasma cfDNA was extracted after various storage durations and temperatures, with aliquots from immediately processed blood subsequently stored at -80°C for different time intervals. Except for LINE1-97, most biomarkers showed significantly higher levels in plasma isolated from whole blood stored at room temperature compared to plasma processed immediately. Storage at 4°C resulted in fragment-specific effects: ALU-247/115 levels remained stable at 3 hours but decreased at 6 hours, while LINE1-266/97 levels increased at both time points. For plasma stored at -80°C, ALU-derived biomarkers remained stable for up to 12 months; however, LINE1-97 levels significantly declined, accompanied by a corresponding increase in LINE1-266/97 as early as one month after freezing. These findings indicate that both storage duration and temperature significantly impact the measured levels of ALU- and LINE1-derived cfDNA biomarkers. Consequently, standardization of pre-analytical handling of blood and plasma is crucial for studies evaluating cfDNA quantity and integrity
A remarkable year for NSCLC: Seven new FDA approvals in 2025 across molecular targets
Full text linkNon-small cell lung cancer (NSCLC) remains the leading cause of cancer mortality worldwide; however, precision oncology has fundamentally transformed its treatment landscape. In 2025, seven approvals by the U.S. Food and Drug Administration (FDA) further accelerated biomarker-driven care across critical molecular subsets. These include MET-directed and trophoblast cell-surface antigen-2 (TROP-2) antibody-drug conjugates (ADCs), expanded strategies targeting epidermal growth factor receptor (EGFR), notably those addressing exon 20 insertion mutations, a ROS proto-oncogene 1 (ROS1) inhibitor, and various human epidermal growth factor receptor 2 (HER2) options that encompass both tumor-agnostic and mutation-selected approaches. These advancements underscore the necessity for integrated diagnostics—such as next-generation sequencing (NGS), fluorescence in situ hybridization (FISH), and immunohistochemistry (IHC)—while also emphasizing ongoing challenges in biomarker selection, therapeutic sequencing, and equitable global implementation
STOP algorithm for bedside mechanical ventilation: Standardized, evidence-based management of critically ill patients
Full text linkThe COVID-19 pandemic revealed significant variability in mechanical ventilation training and bedside practices, highlighting the necessity for standardized, actionable protocols. This study aimed to develop the Standard Training and Operating Procedure (STOP), an evidence-based algorithm designed for managing mechanically ventilated critically ill patients and troubleshooting patient-ventilator interactions. Utilizing the Successive Approximation Model (SAM), we reviewed current guidelines and expert recommendations, created a minimum-viable prototype during a multidisciplinary "savvy start," and refined it through seven iterative review cycles involving 33 frontline clinicians. The finalized tool underwent external evaluation via a Modified-Delphi process within the Checklist for early recognition and treatment of acute illness and injury (CERTAIN) network, engaging 50 clinicians from 19 countries across four continents, with a consensus threshold of ≥70%. STOP consists of eight sequential bedside checkpoints: abnormal vital signs/ventilator alarms, assessment of ventilation adequacy, elevated peak pressure, elevated plateau pressure, lung protection against ventilator-induced lung injury, risk of oxygen toxicity, patient-ventilator asynchrony, and readiness for spontaneous awakening and breathing trials. The Delphi agreement across these steps ranged from 82% to 96%, supporting the tool\u27s face validity and clinical relevance. STOP offers a practical framework to minimize practice variability and enhance the safety of mechanical ventilation; however, prospective implementation studies are necessary to assess its impact on adherence and patient outcomes
Predictors of implant failure: A comprehensive analysis of risk factors in oral implant restoration for patients with partial defects of dentition
Full text linkImplant failure remains a significant challenge in oral implantology, necessitating a deeper understanding of its risk factors to improve treatment outcomes. This study aimed to enhance the clinical outcomes of oral implant restoration by investigating the factors contributing to implant failure in patients with partial dentition defects within two years of treatment. Additionally, the study sought to develop an early risk prediction model for implant failure. A retrospective analysis was conducted on 300 patients with partial dentition defects, dividing them into two groups: a failed implant group and a successful implant group, based on the occurrence of implant failure within two years. General clinical data and condition-specific clinical information were compared between the groups. Multivariate binary logistic regression analysis was used to identify influencing factors, while the predictive effectiveness of the model was assessed using a receiver operating characteristic (ROC) curve. The analysis revealed that factors, such as gender, post-implant smoking, oral hygiene status at the second-year follow-up, tooth position, number of implants, timing of loading, width of keratinized mucosa, and bone quantity significantly influenced the likelihood of implant failure (P < 0.05). Among these, post-implant smoking and tooth position were identified as independent risk factors. The area under the curve (AUC) for tooth position was 0.695, indicating low predictive performance. Although tooth position was determined to be an independent risk factor for implant failure within two years, its predictive performance was limited
Role of gut microbiota and immune response in breast cancer progression
Full text linkBreast cancer is one of the most prevalent cancers among women and is associated with high mortality rates. Emerging evidence suggests a link between gut microbiota and the development of various tumors, particularly those involving immune-mediated mechanisms. However, the potential relationship between gut microbiota and breast cancer—and whether this relationship is mediated by immune cells—remains unclear. This Mendelian randomization (MR) study utilized summary statistics from genome-wide association studies of 412 gut microbiota, 731 immune cell traits, and breast cancer (including its subtypes). Two-sample MR analyses were conducted to assess potential causal relationships between gut microbiota and breast cancer. To further validate the findings, Bayesian weighted MR was applied. Robustness was ensured through sensitivity, specificity, and pleiotropy analyses. A reverse MR analysis was also performed to assess the potential for reverse causality. Finally, mediation analysis was employed to investigate whether immune cells mediate the pathway from gut microbiota to breast cancer. The MR analysis identified 15 gut microbiota and related metabolic pathways significantly associated with breast cancer, with nine showing positive associations and six showing negative associations. The reverse MR analysis did not support a causal effect of breast cancer on gut microbiota. Mediation analysis revealed that DP (CD4⁺CD8⁺) % leukocyte mediated the pathway between gut microbiota (PWY-6263: superpathway of menaquinol-8 biosynthesis II) and breast cancer. These findings suggest a causal relationship between gut microbiota and breast cancer, with a small portion of this effect mediated by immune cells. This study underscores the potential role of gut microbiota and immune modulation in the pathogenesis of breast cancer
Recent advances in stem cell-based therapies for type 1 diabetes: A glimpse into the future
Full text linkType 1 diabetes mellitus (T1DM) is a serious, chronic metabolic and autoimmune disease that affects millions globally. While insulin administration remains the most effective treatment, it is not a cure. Long-term therapies, such as immunotherapy, can be effective for some patients, but they have notable limitations and do not provide a permanent solution. As a result, current research has shifted towards stem cell-based therapies, which offer a potentially expandable and scalable source of pancreatic beta cells. These therapies aim to restore long-term endogenous β-cell function in all T1DM patients, provided they can avoid immune recognition and rejection by the host. In this review, we will discuss the latest first-in-human successes of stem cell therapies for T1DM. We will then explore stem cell-derived islet encapsulation technologies and hypoimmune stem cells, examining how they might overcome the need for immunosuppressive therapy. Additionally, we will provide a summary of recent and ongoing biopharmaceutical industry pipelines and clinical trials for stem cell therapies aimed at treating T1DM. These advances suggest that stem cell therapies offer a promising and highly effective approach to treating patients with this chronic disease. However, large-scale clinical trials over the long term are necessary to verify these early successes and assess the curative potential of stem cell therapy for T1DM
Tumor hypoxia: Classification, detection, and its critical role in cancer progression
Full text linkHypoxia is a common feature of solid tumors and plays a critical role in cancer progression. A thorough understanding of tumor hypoxia is essential for gaining deeper insights into various aspects of cancer biology. This review examines the key factors contributing to tumor hypoxia, such as inadequate blood supply and oxygen delivery resulting from rapid tumor growth. We present a detailed classification of hypoxic regions and provide an overview of current methods used to identify these areas—from molecular techniques to imaging approaches—offering a comprehensive and multifaceted perspective. Additionally, we explore the mechanisms by which hypoxia drives tumor progression. Under low-oxygen conditions, tumor cells can alter their biological behavior, influencing processes such as cell proliferation, immune evasion, and the maintenance of tumor stem cells. By addressing these dimensions, we aim to enhance understanding of how hypoxia contributes to cancer development. Through this in-depth exploration, we hope this review will offer valuable insights to guide future research and clinical applications
OncoImmune machine-learning model predicts immune response and prognosis in leiomyosarcoma
Full text linkLeiomyosarcoma (LMS) is one of the most aggressive tumors originating from smooth muscle cells, characterized by a high recurrence rate and frequent distant metastasis. Despite advancements in targeted therapies and immunotherapies, these interventions have failed to significantly improve the long-term prognosis for LMS patients. Here, we identified OncoImmune differential expressed genes (DEGs) that influence monocytes differentiation and the progression of LMS, revealing varied immune activation states of LMS patients. Using a machine learning approach, we developed a prognostic model based on OncoImmune hub DEGs, which offers a moderate accuracy in predicting risk levels among LMS patients. Mechanistically, we found that ATRX mutation may regulate coiled-coil domain-containing protein 69 (CCDC69) expression, leading to functional alterations in mast cells and immune unresponsiveness through the modulation of various immune-related signaling pathways. This machine learning-based prognostic model, centered on seven OncoImmune hub DEGs, along with ATRX gene status, represents promising biomarkers for predicting prognosis, molecular characteristics, and immune features in LMS
Unveiling etiology and mortality risks in community-acquired pneumonia: A machine learning approach
Full text linkCommunity-acquired pneumonia (CAP) is associated with high mortality, and accurate diagnosis and risk prediction are essential for improving patient outcomes. Traditional diagnostic methods have limitations, prompting the use of machine learning (ML) to enhance diagnostic precision and treatment strategies. This study aims to develop ML models to predict CAP etiology and mortality using clinical data to enable early intervention. A retrospective cohort study was conducted on 251 adult CAP patients admitted to two Jordanian hospitals between March 2021 and February 2024. Various clinical data were analyzed using ML techniques, including linear regression, random forest, SHapley Additive exPlanations (SHAP), lasso regression, mutual information analysis, logistic regression, and correlation analysis. Key predictors of CAP survival included zinc, vitamin C, enoxaparin, and insulin bolus. Mutual information analysis identified neutrophils, alanine transaminase, mean corpuscular volume, hemoglobin, and platelets as significant mortality predictors, while lasso regression highlighted meropenem, arterial blood gases, PCO₂, and platelet count. Logistic regression confirmed intensive care unit (ICU) stay, pH, pulmonary severity index, white blood cell (WBC) count, and bicarbonate levels as crucial variables. Interestingly, lymphocyte count emerged as the strongest predictor of bacterial CAP, conflicting with established knowledge that associates neutrophils with bacterial infections. However, findings related to HCO₃, blood urea nitrogen, and WBC levels were consistent with clinical expectations. SHAP analysis highlighted basophils and fever as key predictors. Further investigation is needed to resolve conflicting findings and optimize predictive models. ML offers promising applications for CAP prognosis but requires refinement to address discrepancies and improve reliability in clinical decision-making
WFDC3 identified as a prognostic and immune biomarker in pancreatic cancer
Full text linkThe whey acidic protein four-disulfide core (WFDC) family comprises key modulators of tumor initiation and progression, offering significant potential for diagnostic, prognostic, and therapeutic applications. However, the specific role of WFDCs in the oncogenesis of pancreatic cancer (pancreatic adenocarcinoma [PAAD]) remains incompletely understood. To address this, we conducted an initial investigation using comprehensive bioinformatic analyses to evaluate WFDCs expression patterns across multiple tumor types, with a focus on PAAD. Bulk and single-cell RNA sequencing datasets from the TCGA and GEO repositories were analyzed to assess WFDC3 expression in PAAD tissues. Kaplan–Meier survival analysis was employed to determine the prognostic significance of WFDC3. To explore its biological functions and underlying mechanisms, we performed functional enrichment analyses in combination with immune infiltration assessments. Experimental validation included CCK-8 and EdU proliferation assays, transwell migration and invasion tests, immunofluorescence staining, flow cytometry, LDH release assays, Western blotting, and quantitative reverse transcription PCR. A LASSO regression model was also developed to predict PAAD outcomes. Our findings reveal that WFDCs exhibit context-dependent roles in tumor progression. Specifically, WFDC3 expression was significantly elevated in PAAD and associated with poorer patient prognosis. Functionally, WFDC3 promoted PAAD cell metastasis by inducing epithelial–mesenchymal transition and contributed to immune evasion by suppressing T cell cytotoxicity. In conclusion, our study identifies WFDC3 as a pro-oncogenic factor in PAAD progression, highlighting its potential as both a prognostic biomarker and a therapeutic target for regulating metastasis and immune responses in this malignancy