Search Results (245)

Glioblastoma (GBM) is the most aggressive and challenging brain cancer, in terms of diagnosis and therapy. The highly infiltrative glioblastoma stem cells (GSCs) are difficult to visualize and surgically remove with the current diagnostic tools, which often lead to misdiagnosis and false-positive results. In this study, we focused on a groundbreaking tool for specifically visualizing and removing GSCs. We exploited the specific binding of A40s aptamer to EphA2 for the selective delivery of Near-Infrared Dyes (NIR-Dyes), like IR700DX and ICG, both in vitro and in vivo. The A40s aptamer, engineered through the NIR-Dye conjugation, did not affect aptamer binding ability; indeed, A40s-NIR-Dye conjugates bound GLI261 stem-like cells and patient-derived GSCs in vitro; moreover, they induced cell death upon photodynamic therapy treatment (PDT). Additionally, when systemically administrated, the A40s-NIR-Dye conjugates allowed GSC visualization and accumulated in tumor mass. This allows GSCs detection and treatment. Our findings demonstrate the potential use of A40s aptamer as a targeted therapeutic approach and imaging tool in vivo for GSCs, paving the way for improved, more effective, and less invasive GBM management. Full article

: Alzheimer’s disease (AD) is a neurodegenerative disorder closely associated with aging, and its pathogenesis involves the interaction of multidimensional pathophysiologic processes. This review outlines the core mechanisms linking aging and AD. The amyloid cascade hypothesis emphasizes that abnormal deposition of amyloid-β (Aβ) triggers neuronal damage and synaptic dysfunction, which is exacerbated by aging-associated declines in protein clearance. Neuroinflammation, a synergistic pathogenetic factor in AD, is mediated by microglia activation, creating a vicious cycle with Aβ and tau pathology. The cholinergic hypothesis states that the degeneration of cholinergic neurons in the basal forebrain can lead to acetylcholine deficiency, which is directly associated with cognitive decline. Endothelial disorders promote neuroinflammation and metabolic waste accumulation through blood–brain barrier dysfunction and cerebral vascular abnormalities. In addition, glutamate-mediated excitotoxicity and mitochondrial dysfunction (e.g., oxidative stress and energy metabolism imbalance) further lead to neuronal death, and aging-associated declines in mitochondrial autophagy exacerbate such damage. This review also explores the application of animal models that mimic AD and aging in studying these mechanisms and summarizes therapeutic strategies targeting these pathways. Future studies need to integrate multi-targeted therapies and focus on the role of the aging microenvironment in regulating AD pathology in order to develop more effective early diagnosis and treatment options. Full article

The thyroid is a vital endocrine organ that regulates metabolism, heart rate, respiration, digestion, body temperature, brain development, skin and bone maintenance, and reproduction and fertility. Thyroid cancer (TC) is the most common endocrine malignancy, with an estimate of 44,020 new cases in 2025. Incidence has been increasing, most notably at 4–5% per year in young adults. Papillary thyroid cancer (PTC), the most common TC subtype, accounts for approximately 80% of newly diagnosed TC cases. Furthermore, 2290 deaths are expected from the disease in 2025, with survival at over 98% with treatment. However, as PTC occurs most frequently in young women, recurrences are frequent and the 10-year disease-specific survival rate for advanced PTC is less than 50%. This narrative review aims to describe the current understanding of the thyroid gland, the incidence and subtypes of thyroid cancer, and specifically the diagnosis, prognosis, treatment, and recurrence of PTC. This is supplemented by the role of molecular pathways and biomarkers in PTC. Full article

Background and Objectives: Despite the latest advancements in interventional procedures and pharmacological therapy, the incidence of heart failure and death rate following an acute myocardial remain unacceptably high. This study was designed in response to the limited and conflicting literature data regarding the diagnostic and prognostic role of modern inflammatory biomarkers in patients with coronary artery disease. Materials and Methods: We conducted a case–control, prospective observational study. A total of 145 patients were analyzed, of whom 105 patients had an acute coronary syndrome diagnosis and represented the study group, while 40 patients with a chronic coronary syndrome diagnosis represented the control group. This study investigates the diagnostic and prognostic role of the interleukin 1β (IL-1β), interleukin 6 (IL-6), interleukin 10 (IL-10), Growth differentiation factor 15 (GDF-15), and classic biomarkers in patients with ischemic coronary heart disease. Results: IL-1β exhibited a prognostic role, being significantly correlated with a left ventricular ejection fraction below 30%. GDF-15 plays a dual role, as a cardio-inflammatory biomarker, being significantly correlated with both N-terminal pro-brain natriuretic peptide (NT-proBNP), and IL-1β, IL-6, and CRP. At the same time, GDF-15 represents a surrogate marker for renal dysfunction. According to the ROC analysis, patients at high mortality risk can be identified with adequate accuracy by cardiac troponin, GDF-15, and IL-10, in addition to NT-proBNP. Logistic regression models confirmed NT-proBNP and IL-10 as mortality predictors. Conclusions: IL-1β stands out for its significant prognostic role, while IL-6 did not demonstrate a diagnostic or prognostic role in acute myocardial infarction patients. IL-10 demonstrated superior predictive value in terms of fatal prognosis compared with the other modern biomarkers. GDF-15 is representative of a multivalent biomarker involved in inflammation, heart failure, and renal dysfunction. Full article

Background: Real-world data provide insights into populations underrepresented in clinical trials such as non-small-cell cancer (NSCLC) patients with brain metastases (BMs). Despite global survival improvement due to novel drug employment, their impact in resource-limited settings like Serbia remains underexplored. This study analyzes the overall survival (OS) of NSCLC patients with BMs treated in routine clinical practice, considering patient-, disease- and treatment-related factors amid restricted access to novel drugs. Methods: We retrospectively analyzed 267 NSCLC patients diagnosed with BMs from 2018 to 2022 at a single Serbian clinical center. Inclusion required histologically confirmed NSCLC, radiologically verified BMs and complete clinical data. OS was defined as the time from BM verification to death or last follow-up. Kaplan–Meier curves and Cox regression were used for survival analysis. Results: Median OS (mOS) was 5.0 months. Univariate analysis linked age < 65 years, female gender, single BM, asymptomatic BMs, ECOG PS 0–1, BM verification at diagnosis and combined systemic and local therapy to better OS. Combined therapy offered the best survival rates (mOS: 9.0 months), while best supportive care and local-only therapy both resulted in a poor mOS of 2.0 months. Immunotherapy and targeted therapy were associated with the highest mOS, outperforming chemotherapy alone (13.0 vs. 7.0 months, p < 0.001). Multivariate analysis confirmed younger age, single BM, early BM verification and combined therapy as independent predictors of improved survival. Conclusions: limited access to novel therapies remains associated with poor patient survival, highlighting the need for better global availability. Full article

Background/Objectives: Human glioblastoma (GBM) is the most aggressive brain cancer in adults and a highly treatment-refractory malignancy. The overall prognosis for the GBM is extremely poor, with a median survival of 12–14 months after initial diagnosis. Many GBM patients initially respond to the DNA alkylating agent temozolomide (TMZ), but patients often become therapy-resistant, and tumors recur. We previously reported that treatment with PFI-3, which is a small molecule inhibitor of the bromodomain of the BRG1 subunit of the SW1/SNF chromatin remodeling complex, enhanced the sensitivity of GBM cells to TMZ in vitro and in vivo GBM animal models. Our general objective was to perform an SAR study of new diphenyl PFI-3 analogs. Methods: New structural analogs of PFI-3 were developed, synthesized, and tested for their ability to enhance TMZ-induced GBM cell death by ELISA. Results: Following on the enhanced activity of compounds 2a and 2b, new diphenyl PFI-3 analogs with specific structural adjustments were made to better understand the structural requirements to optimize function. Additionally, several new structurally different candidates (e.g., 4a, 4b, and 5) showed much better efficacy in sensitizing GBM cells to TMZ-induced GBM cell death. Conclusions: Four series of PFI-3 analogs (2, 3, 4, and 5) were designed, synthesized, and tested for the ability to sensitize GBM cells to TMZ-induced cell death. Series 2 optimized the A-ring and R-isomer chirality. Series 3 used a 5-membered linker with weak activity. Series 4’s di-phenyl urea compounds showed better bromodomain inhibition. Series 5’s methoxyphenyl-B-ring analogs were exceptionally strong inhibitors. Full article

Background: Traumatic brain injury (TBI) is one of the main causes of death in trauma pathology, especially among the youngest victims. After having evaluated the causality relationship between damage to the brain tissue and death, pathologists should try to estimate the duration between the TBI and death. Immunohistochemistry could be used in this field as a personalized medico-legal approach. This study aims to evaluate the possible role of vimentin and p53 as TBI markers to assess vitality and date the TBI. Methods: Twelve cases of TBI deaths were selected (two women and ten men, with a mean age of 46.83 years). In seven cases, death occurred immediately after the trauma, while in the others, death occurred after some days. An immunohistological study of brain samples using anti-p53 and anti-vimentin antibodies was performed. A semi-quantitative scale was adopted to grade the immunohistochemical reaction. Results: Our results showed a strong relationship between the p53 immunoreaction grade and TBI (X-squared value 10.971, p-value < 0.01), suggesting that p53 expression is enhanced in TBI cases. Vimentin is more expressed when the PTI is longer. Vimentin-immunoreaction was weaker than p53-immunoreaction (+0.75 vs. +1.83 mean values, respectively) in a group predominantly including short post-traumatic interval cases. Conclusions: The present research is limited by the small sample size; however, the molecules tested, vimentin and p53, have shown great potential to be used, in addition to others, as biological markers for the diagnosis and timing of TBI. Full article

Background/Objectives: No prior studies have attempted to identify a biomarker for initial brain metastasis velocity (iBMV), with limited studies attempting to correlate genomic data with the development of brain metastases. Methods: Patients with non-small-cell lung cancer (NSCLC) who underwent next-generation sequencing (NGS) were identified in our departmental database. iBMV was calculated by dividing the number of BMs by the interval of time between primary cancer and BM diagnosis. Two-sample t-testing was used to identify mutations statistically associated with iBMV (p < 0.1). A value of +1 was assigned to each mutation with a positive association (“deleterious genes”), and a value of −1 to each with an inverse association (“protective genes”). The sum of these values was calculated to define iBMV risk scores of −1, 0 and 1. Pearson correlation test was used to determine the association between iBMV risk score and calculated iBMV, and a competing risk analysis assessed for death as a competing risk to the development of BMs. Results: A total of 312 patients were included in the analysis, 218 of whom (70%) developed brain metastases. “Deleterious genes” included ARID1A, BRAF, CDK4, GNAQ, MLH1, MSH6, PALB2, RAD51D, RB1 and TSC1; “protective genes” included ARAF, IDH1, MYC, and PTPN11. iBMV risk scores of 1, 0 and −1, predicted an 88%, 61% and 65% likelihood of developing a BM (p < 0.01). A competing risk analysis found a significant association between iBMV risk scores of 1 vs. 0 and 1 vs. −1, and the likelihood of developing a BM using death as a competing risk. Overall survival (OS) at 1 and 2 years for patients with iBMV risk scores of 1, 0 and −1 was 72% vs. 84% vs. 85% and 46% vs. 69% vs. 70% (p < 0.02). Conclusions: Development of a genomic signature for iBMV via non-invasive liquid biopsy appears feasible in NSCLC patients. Patients with a positive iBMV risk score were more likely to develop brain metastases. Validation of this signature could lead to a biomarker with the potential to guide treatment recommendations and surveillance schedules. Full article

Background/Objectives: Breast cancer is the most common malignancy in women and the second leading cause of cancer-related deaths globally. It is also the second most frequent source of brain metastases (BMs), contributing to 5–20% of cases. Despite this, routine brain imaging for screening is not recommended and is only conducted when clinical symptoms or physical findings suggest metastasis. This study aims to identify clinical predictors associated with overall survival (OS) and the timing of BM development in breast cancer patients. Methods: We performed a retrospective review of medical records for 113 patients diagnosed with BMs secondary to breast cancer at our institution between 2000 and 2020. Baseline demographic data and clinical characteristics related to BMs were collected. To identify factors associated with OS and time to BM development after breast cancer diagnosis, we conducted univariate analysis using Kaplan–Meier curves, bivariate analysis with the log-rank test, and multivariate analysis via the Cox Proportional Hazard model. Results: An early diagnosis of BMs was identified as a significant predictor of prolonged OS (aHR = 0.22; 95% CI: 0.049–0.98, p = 0.05). Post-menopausal status at breast cancer diagnosis (aHR = 1.69; 95% CI: 1.13–2.53, p = 0.01), Asian ethnicity (aHR = 2.30; 95% CI: 1.03–5.16, p = 0.04), and the ER+/HER2+ subtype (aHR = 2.06; 95% CI: 1.14–3.71, p = 0.02) were significantly associated with a shorter time to BM diagnosis. A subgroup analysis of patients with ER+ breast tumors revealed that Hispanic or Arabic ethnicity (aHR = 3.63; 95% CI: 1.34–9.81, p = 0.01) and stage IV diagnosis (aHR = 2.09; 95% CI: 1.16–3.76, p = 0.01) were significantly associated with shorter intervals to BM diagnosis. Conclusions: Breast cancer remains a significant global health burden for women, yet clear guidelines for routine BMs screening are still lacking. Early detection of BMs has been shown to notably improve long-term survival outcomes. Additionally, post-menopausal status, Hispanic or Arabic ethnicity, and the HER2+ tumor subtype are associated with shorter time to BM development, highlighting these factors as potential indicators for central nervous system screening. Full article

Background/Objectives: While ancillary tests for brain death diagnosis are not routinely recommended in guidelines, they may be necessary in specific clinical scenarios. Computed tomography angiography (CTA) is particularly advantageous in pediatric patients due to its noninvasive nature, accessibility, and rapid provision of anatomical information. This study aims to assess the diagnostic sensitivity of a revised venous system (ICV-SPV) utilizing a 4-point scoring system in children clinically diagnosed with brain death. Materials and Methods: A total of 43 pediatric patients clinically diagnosed with brain death who underwent CTA were retrospectively analyzed. Imaging was performed using a standardized brain death protocol. Three distinct 4-point scoring systems (A20-V60, A60-V60, ICV-SPV) were utilized to assess vessel opacification in different imaging phases. To evaluate age-dependent sensitivity, patients were categorized into three age groups: 26 days–1 year, 2–6 years, and 6–18 years. The sensitivity of each 4-point scoring system in diagnosing brain death was calculated for all age groups. Results: The revised venous scoring system (ICV-SPV) demonstrated the highest overall sensitivity in confirming brain death across all age groups, significantly outperforming the reference 4-point scoring systems. Furthermore, the ICV-SPV system exhibited the greatest sensitivity in patients with cranial defects. Conclusions: The revised 4-point venous CTA scoring system, which relies on the absence of ICV and SPV opacification, is a reliable tool for confirming cerebral circulatory arrest in pediatric patients with clinical brain death. Full article

Traumatic brain injury (TBI) is a leading cause of disability and death globally, presenting significant challenges for diagnosis, prognosis, and treatment. As healthcare technology advances, artificial intelligence (AI) has emerged as a promising tool in enhancing TBI rehabilitation outcomes. This literature review explores the current and potential applications of AI in TBI management, focusing on AI’s role in diagnostic tools, neuroimaging, prognostic modeling, and rehabilitation programs. AI-driven algorithms have demonstrated high accuracy in predicting mortality, functional outcomes, and personalized rehabilitation strategies based on patient data. AI models have been developed to predict in-hospital mortality of TBI patients up to an accuracy of 95.6%. Furthermore, AI enhances neuroimaging by detecting subtle abnormalities that may be missed by human radiologists, expediting diagnosis and treatment decisions. Despite these advances, ethical considerations, including biases in AI algorithms and data generalizability, pose challenges that must be addressed to optimize AI’s implementation in clinical settings. This review highlights key clinical trials and future research directions, emphasizing AI’s transformative potential in improving patient care, rehabilitation, and long-term outcomes for TBI patients. Full article

According to the World Health Organization (WHO), peripheral and central neurological disorders affect approximately one billion people worldwide. Ischemic stroke and Alzheimer’s Disease and other dementias are the second and fifth leading causes of death, respectively. In this context, detecting and classifying brain lesions constitute a critical area of research in medical image processing, significantly impacting clinical practice. Traditional lesion detection, segmentation, and feature extraction methods are time-consuming and observer-dependent. In this sense, research in the machine and deep learning methods applied to medical image processing constitute one of the crucial tools for automatically learning hierarchical features to get better accuracy, quick diagnosis, treatment, and prognosis of diseases. This project aims to develop and implement deep learning models for detecting and classifying small brain White Matter hyperintensities (WMH) lesions in magnetic resonance images (MRI), specifically lesions concerning ischemic and demyelination diseases. The methods applied were the UNet and Segmenting Anything model (SAM) for segmentation, while YOLOV8 and Detectron2 (based on MaskRCNN) were also applied to detect and classify the lesions. Experimental results show a Dice coefficient (DSC) of 0.94, 0.50, 0.241, and 0.88 for segmentation of WMH lesions using the UNet, SAM, YOLOv8, and Detectron2, respectively. The Detectron2 model demonstrated an accuracy of 0.94 in detecting and 0.98 in classifying lesions, including small lesions where other models often fail. The methods developed give an outline for the detection, segmentation, and classification of small and irregular morphology brain lesions and could significantly aid clinical diagnostics, providing reliable support for physicians and improving patient outcomes. Full article

Background/Objective: Alzheimer’s disease is a progressive brain syndrome causing cognitive decline and, ultimately, death. Early diagnosis is essential for timely medical intervention, with MRI medical imaging serving as a primary diagnostic tool. Machine learning (ML) and deep learning (DL) methods are increasingly utilized to analyze these images, but accurately distinguishing between healthy and diseased states remains a challenge. This study aims to address these limitations by developing an integrated approach combining swarm intelligence with ML and DL techniques for Alzheimer’s disease classification. Method: This proposal methodology involves sourcing Alzheimer’s disease-related MRI images and extracting features using convolutional neural networks (CNNs) and the Gray Level Co-occurrence Matrix (GLCM). The Harris Hawks Optimization (HHO) algorithm is applied to select the most significant features. The selected features are used to train a multi-layer perceptron (MLP) neural network and further processed using a long short-term (LSTM) memory network in order to classify tumors as malignant or benign. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset is utilized for assessment. Results: The proposed method achieved a classification accuracy of 97.59%, sensitivity of 97.41%, and precision of 97.25%, outperforming other models, including VGG16, GLCM, and ResNet-50, in diagnosing Alzheimer’s disease. Conclusions: The results demonstrate the efficacy of the proposed approach in enhancing Alzheimer’s disease diagnosis through improved feature extraction and selection techniques. These findings highlight the potential for advanced ML and DL integration to improve diagnostic tools in medical imaging applications. Full article

Background: Human central nervous system infections due to free-living nematodes, although extremely rare, are usually fatal. Immunodeficiency has not been a feature of most of these cases, unlike the situation pertaining to disseminated Strongyloides stercoralis infection. Case report: An elderly immunocompetent man presented with a history of tinnitus and otalgia, progressing to central nervous system involvement with confusion, weakness, and other neurological signs. Examination revealed a unilateral external auditory canal soft tissue mass and radiological evidence of ipsilateral temporal bone destruction and brain parenchymal disease. A biopsy of the ear canal mass revealed the presence of an unidentified nematode species, and treatment with anthelminthics was started. The patient’s clinical condition deteriorated and he died shortly after admission to the intensive care unit. The immediate cause of death was bronchopneumonia. During the autopsy, an extensive involvement of the right middle cranial fossa was found, with destruction of the squamous and petrous parts of the temporal bone. Results: We identified adult, larval, and egg stages of a free-living nematode in the antemortem external auditory canal tissue mass and the post-mortem brain samples. Polymerase chain reaction assays, with Sanger and whole-genome sequencing, identified Cephalobus cubaensis. This is a free-living species not previously known to be pathogenic to humans, although nematodes of the same genus have caused mastitis in horses. Conclusions: Microscopic appearance and the invasive behaviour of the pathogen evoked a putative diagnosis of Halicephalobus gingivalis, the most frequently reported free-living nematode infecting humans. However, this nematode’s size and anatomical features, and the clinical presentation and duration of illness, prompted the consideration of an alternative species. We speculate that an initial bacterial otitis externa provided the opportunity for colonization by the nematode from an environmental source and subsequent invasion. Full article

Neurodegenerative disorders (NDs) cause progressive neuronal loss and are a significant public health concern, with NDs projected to become the second leading global cause of death within two decades. Huntington’s disease (HD) is a rare, progressive ND caused by an autosomal-dominant mutation in the huntingtin (HTT) gene, leading to severe neuronal loss in the brain and resulting in debilitating motor, cognitive, and psychiatric symptoms. Given the complex pathology of HD, biomarkers are essential for performing early diagnosis, monitoring disease progression, and evaluating treatment efficacy. However, the identification of consistent HD biomarkers is challenging due to the prolonged premanifest HD stage, HD’s heterogeneous presentation, and its multiple underlying biological pathways. This study involves a 10-year bibliometric analysis of HD biomarker research, revealing key research trends and gaps. The study also features a comprehensive literature review of emerging HD biomarkers, concluding the need for better stratification of HD patients and well-designed longitudinal studies to validate HD biomarkers. Promising candidate wet HD biomarkers— including neurofilament light chain protein (NfL), microRNAs, the mutant HTT protein, and specific metabolic and inflammatory markers— are discussed, with emphasis on their potential utility in the premanifest HD stage. Additionally, biomarkers reflecting brain structural deficits and motor or behavioral impairments, such as neurophysiological (e.g., motor tapping, speech, EEG, and event-related potentials) and imaging (e.g., MRI, PET, and diffusion tensor imaging) biomarkers, are evaluated. The findings underscore that the discovery and validation of reliable HD biomarkers urgently require improved patient stratification and well-designed longitudinal studies. Reliable biomarkers, particularly in the premanifest HD stage, are crucial for optimizing HD clinical management strategies, enabling personalized treatment approaches, and advancing clinical trials of HD-modifying therapies. Full article