Search Results (5,440)

Objective: Non-oncological musculoskeletal chronic pain has a high prevalence and is a cause of disability, reduced quality of life, and significant economic impact. Physical exercise is presented as a treatment option; however, pain measurement remains a challenge, and various biomarkers are potential candidates to objectify this process. This systematic review aims to study the effect of physical exercise on non-oncological musculoskeletal chronic pain and its associated biomarkers based on randomized controlled trials. Methods: A search for randomized controlled trials was conducted in the PubMed, Web of Science, and Scopus databases based on the established inclusion and exclusion criteria, along with a risk of bias assessment following the recommendations of the Cochrane Collaboration. Results: Five studies investigated various physical exercise interventions and their effects on biomarkers linked to chronic pain. Exercise consistently reduced self-reported pain, though no clear overall correlation with biomarker changes was found. However, significant associations emerged for specific biomarkers, particularly inflammatory markers and those identified through structural and functional brain imaging, suggesting potential mechanisms underlying pain modulation. Conclusions: The findings suggest that identifying chronic pain variations through biomarkers requires selecting markers linked to immune activity or brain processes. More randomized controlled trials with sufficient sample sizes and rigorous methodologies are needed. Despite this, physical exercise remains a valuable intervention for managing non-oncological musculoskeletal chronic pain. Additionally, it holds potential as a tool for uncovering novel biomarkers that may contribute to the objectification and understanding of chronic pain mechanisms. Full article

Background: The epidermal growth factor receptor (EGFR) is an important factor in the behavior of diffuse glioma, serving as a potential biomarker for tumor aggressiveness and a therapeutic target. Diffusion tensor imaging (DTI) provides insights into the microstructural integrity of brain tissues, allowing for detailed visualization of tumor-induced changes in white matter tracts. This imaging technique can complement molecular pathology by correlating imaging findings with molecular markers and genetic profiles, potentially enhancing the understanding of tumor behavior and aiding in the formulation of targeted therapeutic strategies. The present study aimed to investigate the molecular properties of diffuse glioma based on DTI sequences. Methods: A total of 27 patients with diffuse glioma (in accordance with the WHO 2021 classification) were investigated using preoperative DTI sequences. The study was conducted using the tractography software DSI Studio (Hou versions 2025.04.16). Following the preprocessing of the raw data, volumes of the arcuate fasciculus (AF), frontal aslant tract (FAT), inferior fronto-occipital fasciculus (IFOF), superior longitudinal fasciculus (SLF), and uncinate fasciculus (UF) were reconstructed, and fractional anisotropy (FA) was derived. Molecular pathological examination was conducted to assess the presence of EGFR amplifications. Results: The mean age of patients was 56 ± 13 years, with 33% females. EGFR amplification was observed in 8/27 (29.6%) of cases. Following correction for multiple comparisons, FA in the left AF (p = 0.025) and in the left FAT (p = 0.020) was found to be significantly lowered in EGFR amplified glioma. In the right language network, however, no statistically significant changes were observed. Conclusions: EGFR amplification may be associated with lower white matter integrity of left hemispheric language tracts, possibly impairing neurological function and impacting surgical outcomes. The underlying molecular and cellular mechanisms driving this association require further investigation. Full article

Background: Blood pressure variability (BPV) has emerged as an important clinical factor in acute ischemic stroke (AIS), with evidence linking excessive fluctuations in systolic blood pressure (SBP) to secondary brain injury. This study aimed to assess the association between SBP variability during the first week of hospitalization and the risk of early post-stroke complications, specifically hemorrhagic transformation and infarct growth. Methods: We conducted a prospective cohort study involving 138 AIS patients admitted to the Pius Brinzeu County Emergency Hospital, Timișoara, between November 2022 and December 2024. Systolic blood pressure (SBP) was assessed three times daily over a period of seven days, with variability determined as the standard deviation (SD) of the recorded values. Patients were categorized based on treatment modality (conservative versus intravenous thrombolysis), and complications were evaluated using repeated computed tomography (CT) imaging. Results: SBP variability was significantly higher in patients who developed hemorrhagic transformation (OR 3.64, 95% CI: 2.21–5.99, p < 0.001) or infarct growth (OR 1.80, 95% CI: 1.24–2.61, p = 0.001). A monotonic trend was observed across SBP variability categories, with complication rates increasing significantly with higher variability levels (p < 0.001 for hemorrhagic transformation; p = 0.001 for infarct growth). In multivariable models, SBP variability remained an independent predictor of hemorrhagic transformation in both the conservative group (OR 4.78, 95% CI: 2.07–37.14, p = 0.02) and thrombolysis group (OR 1.47, 95% CI: 1.13–2.08, p = 0.01), and was also associated with infarct growth in the thrombolysis group (OR 1.51, 95% CI: 1.13–2.25, p = 0.02). Conclusions: Medium-term SBP variability is an independent predictor of early ischemic and hemorrhagic complications following AIS, particularly in patients receiving thrombolysis. These findings support the need for targeted strategies to stabilize BP during the acute phase of stroke care. Full article

This review provides a mechanistic framework to strategically design nanoparticles capable of efficiently crossing the blood–brain barrier (BBB), a critical limitation in neurological treatments. We systematically analyze nanoparticle–BBB transport mechanisms, including receptor-mediated transcytosis, adsorptive-mediated transcytosis, and transient barrier modulation. Essential nanoparticle parameters (size, shape, stiffness, surface charge, and biofunctionalization) are evaluated for their role in enhancing brain targeting. For instance, receptor-targeted nanoparticles can significantly enhance brain uptake, achieving levels of up to 17.2% injected dose per gram (ID/g) in preclinical glioma models. Additionally, validated preclinical models (human-derived in vitro systems, rodents, and non-human primates) and advanced imaging techniques crucial for assessing nanoparticle performance are discussed. Distinct from prior BBB nanocarrier reviews that primarily catalogue mechanisms, this work (i) derives quantitative ‘design windows’ (size 10–100 nm, aspect ratio ~2–5, near-neutral ζ) linked to transcytosis efficiency, (ii) cross-walks human-relevant in vitro/in vivo models (including TEER thresholds and NHP evidence) into a translational decision guide, and (iii) integrates regulatory/toxicology readiness (ISO 10993-4, FDA/EMA, ICH) into practical checklists. We also curate recent (2020–2025) %ID/g brain-uptake data across lipidic, polymeric, protein, inorganic, and hybrid vectors to provide actionable, evidence-based rules for BBB design. Full article

Background: Adeno-associated viral (AAV) vectors are the leading platform for gene therapy, but common delivery routes show limited spread to distal cortical structures, hence the utility of direct, intrathalamic infusions for broader transgene distribution. In this preliminary study, we recapitulate previous studies targeting the thalamus as a conduit to achieve cortical transgene spread and showcase novel data evaluating biodistribution of a green fluorescent protein (GFP) using cryo-fluorescence tomography (CFT). For the first time in nonhuman primates (NHPs) and coupled with magnetic resonance imaging (MRI)-guidance, we demonstrated the application of CFT as a powerful tool to map out vector distribution in the NHP brain. Methods: Briefly, a single thalamic infusion was performed in African green monkeys using ClearPoint’s navigational platform to deliver an AAV serotype 2 vector containing a GFP payload. Transgene biodistribution was assessed in the left and right hemispheres using CFT and histological analysis, respectively. Results: Infusions were successfully performed with sub-millimetric target accuracy and with minimal error, achieving ~86% thalamic coverage with the largest infusion volume. Histology confirmed the presence of the GFP transgene, with the strongest signal in the cerebral gray/white matter and internal capsule, while CFT allowed for the three-dimensional detection of the transgene starting at the site of infusion and spreading to multiple cortical regions. Conclusions: These findings suggest that by combining MRI-guided technology with CFT imaging, it is feasible to map whole-brain gene biodistribution in NHPs. This proof-of-concept study bridges the gap between cellular microscopy and MRI-guidance to provide a complete picture of disease and treatment with clinical applicability. Full article

Background: Brain computed tomography (CT) is the primary imaging modality for patients with acute neurological complaints in emergency departments, despite having a low diagnostic yield for many conditions. This study aimed to assess the common indications for brain CT, evaluate the prevalence of acute pathologies, and explore whether certain patient groups may be overexposed to unnecessary scans, impacting both patient safety and healthcare costs. Methods: We conducted a retrospective review of brain CT requests from the General Emergency Department in a single center over a one-month period. We recorded patient demographics (sex, age), scan indications, presence of focal neurological symptoms, acute pathology on CT, and final diagnoses. Descriptive statistics, including means ± SEM, were calculated using GraphPad Prism version 10.4.1. Results: A total of 584 brain CT scans were requested, of which 532 (91.1%) were normal, and 52 (8.9%) showed acute pathology. The age of all included patients were 70.8 ± 0.7 years with women (n = 304, 52.1%) being 71.9 ± 1.0 years old and men (n = 280, 47.9%) 69.7 ± 1.0 years old (p > 0.1). The most common indication for CT was head trauma (265, 45.4%) followed by ischemic stroke (130, 22.3%). The most frequent pathologies were ischemic stroke (2.7%), subdural hematoma (1.7%), and other traumatic bleeds (1.7%). Of the 52 patients with acute pathology, 42 (80.8%) exhibited focal neurological deficits. Conclusions: 91.1% of the brain CT scans in the emergency department were normal and did not lead to further intervention. While this may indicate a low diagnostic yield in certain patient groups—particularly those presenting with mild or nonspecific neurological symptoms—it does not alone confirm overuse. These findings highlight the importance of careful clinical evaluation to optimize imaging decisions. Reducing potentially unnecessary brain CT scans could lower healthcare costs and minimize radiation exposure, but the health-economic impact depends on balancing the savings with the potential costs of missing critical diagnoses and the associated societal consequences. Full article

Background/Objectives: Glioblastomas (GBMs) and brain metastases (BMs) are both frequent brain lesions. Distinguishing between them is crucial for suitable therapeutic and follow-up decisions, but this distinction is difficult to achieve, as it includes clinical, radiological and histopathological correlation. However, non-invasive AI examination of conventional and advanced MRI techniques can overcome this issue. Methods: We retrospectively selected 78 patients with confirmed GBM (39) and single BM (39), with conventional MRI investigations, consisting of T2W FLAIR and CE T1W acquisitions. The MRI images (DICOM) were evaluated by an AI segmentation tool, comparatively evaluating tumor heterogeneity and peripheral edema. Results: We found that GBMs are less edematous than BMs (p = 0.04) but have more internal necrosis (p = 0.002). Of the BM primary cancer molecular subtypes, NSCCL showed the highest grade of edema (p = 0.01). Compared with the ellipsoidal method of volume calculation, the AI machine obtained greater values when measuring lesions of the occipital and temporal lobes (p = 0.01). Conclusions: Although extremely useful in radiomics analysis, automated segmentation applied alone could effectively differentiate GBM and BM on a conventional MRI, calculating the ratio between their variable components (solid, necrotic and peripheral edema). Other studies applied to a broader set of participants are necessary to further evaluate the efficacy of automated segmentation. Full article

Background: Accurate and early diagnosis of autism spectrum disorder (ASD) is essential for timely intervention. Structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) provide complementary insights into brain structure and function. Most deep learning approaches rely on a single modality, limiting their ability to capture cross-modal relationships. Methods: We propose DAGMNet, a dual-branch attention-pruned graph neural network for ASD prediction that integrates sMRI, fMRI, and phenotypic data. The framework employs modality-specific feature extraction to preserve unique structural and functional characteristics, an attention-based cross-modal fusion module to model inter-modality complementarity, and a phenotype-pruned dynamic graph learning module with adaptive graph construction for personalized diagnosis. Results: Evaluated on the ABIDE-I dataset, DAGMNet achieves an accuracy of 91.59% and an AUC of 96.80%, outperforming several state-of-the-art baselines. To validate the method’s generalizability, we also validate it on ADNI datasets from other degenerative diseases and achieve good results. Conclusions: By effectively fusing multimodal neuroimaging and phenotypic information, DAGMNet enhances cross-modal representation learning and improves diagnostic accuracy. To further assist clinical decision making, we conduct biomarker detection analysis to provide region-level explanations of our model’s decisions. Full article

Background/Objectives: Several sequences for magnetization transfer contrast (MTC) imaging are available, from indices of MTC ranging from quantitative magnetization transfer (qMT) that yields the macromolecular fraction to simple ratios of signal intensities with and without a magnetization transfer (MT) pulse. Aging muscle undergoes changes including an increase in fibrosis and adipose accompanied by fiber atrophy and loss. The objective is to evaluate five MTC sequences to study age-related differences in muscle tissue composition. Methods: The lower leg (calf) of 15 young (8M/7F, 25.8 ± 3.7 years) and 9 senior subjects (5F/4M, 68.4 ± 3.3 years) was imaged with the following sequences: multi-offset qMT fit to the Ramani and Yarnykh models, single-offset qMT two-parameter fit to the Ramani model, a semi-quantitative MT_sat sequence, magnetization transfer ratio (MTR), and MTR-corrected (MTR_corr) for B1 inhomogeneities. T1 mapping was also performed. Statistical analysis was performed to identify significant age-related and regional (intermuscular) differences. Results: Significant age-related decreases (p < 0.001) in macromolecular fraction (from two-parameter fit), MT_sat, MTR, and MTR_corr were identified. A significant age-related increase in T1 (p < 0.001) was also identified. Pearson correlation coefficients between T1 and MTC indices were weak to moderate but significant. Conclusions: Age-related decreases in MTC may reflect that loss of myofibrillar proteins dominates the increase in collagen content with age. Further, the modest correlation of MTC indices with T1 indicates that all the age-related differences in MTC cannot be explained by an increase in inflammation. The MT_sat sequence was identified as the most clinically relevant in terms of acquisition speed, post-processing simplicity, and ability to identify age-related differences in macromolecular fractions. Full article

Objectives: To investigate the correlation between intraoperative conventional ultrasound, SWE, and SMI ultrasound manifestations of glioma and the expression of immunohistochemical markers. Methods: Patients with single superficial supratentorial glioma scheduled for brain tumor resection in our neurosurgery department from October 2020 to October 2022 were prospectively included. High-grade glioma (HGG) and low-grade glioma (LGG) were classified by pathological histological grading, and the differences in conventional ultrasound, SWE Young’s modulus, and SMI intratumoral and peritumoral blood flow architecture between HGG and LGG were analyzed, and the SWE diagnostic cut-off value was calculated by the Youdon index. Logistic regression models were used to analyze the independent predictive ultrasound signs associated with the diagnosis of HGG. HGG and LGG were classified by pathological histological grading. IDH1 expression was measured by immunohistochemical methods to analyze the correlation between IDH1 expression in glioma and clinical and ultrasound characteristics. Results: Forty-eight patients with glioma admitted to our hospital from October 2020 to October 2022 were included in this study, including 30 (62.5%) with HGG and 18 (37.5%) with LGG. For conventional ultrasound, HGG was often associated with severe peritumoral edema compared with LGG (p = 0.048). The sensitivity of HGG was 88.9%, the specificity was 86.7%, and the AUC was 0.855 (95% confidence interval: 0.741–0.968, p = 0.001) using Young’s mode 13.90 kPa as the threshold. Logistic analysis showed that SWE Young’s modulus values, and peritumoral and intratumoral SMI blood flow structures, were associated with the diagnosis of HGG. Among the 48 gliomas, 22 (45.8%) were IDH1-positive and 26 (54.2%) were IDH1-negative, with no statistical difference in age between the two groups and a statistical difference in histological grading (p < 0.05). There was a statistical difference between IDH1 mutant and wild type in terms of peritumoral edema and SMI intratumoral and peritumoral tissue vascular architecture. Logistic regression models showed that intratumoral and peritumoral tissue SMI vascular architecture was a valid predictor of IDH1 positivity, with a classification accuracy of 81.3%, sensitivity of 90.9%, and specificity of 73.1%. Further group analysis of mutant Young’s modulus values in LGG were higher than wild-type Young’s modulus values (p = 0.031). Conclusions: Peritumoral and intratumoral tissue SMI vascular architecture was a valid predictor of IDH1 positivity. Based on intraoperative ultrasound multimodality images, we can preoperatively determine the expression of molecular markers of lesions, which is of clinical significance for optimizing surgical strategies and predicting prognosis. Full article

Purpose: This systematic review aims to analyze the literature on the application of AI in predicting patient outcomes and treatment-related toxicity in those undergoing SBRT or SRS across heterogeneous tumor sites. Materials and methods: Our review conformed to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. PubMed, EMBASE and Scopus were systematically searched for English-language human studies evaluating AI for outcome and toxicity prediction in patients undergoing SBRT or SRS for solid tumors. Search terms included (“Stereotactic Body Radiotherapy” OR “SBRT” OR “Stereotactic Radiosurgery” OR “SRS” OR “Stereotactic Ablative Radiotherapy” OR “SABR”) AND (“Artificial Intelligence” OR “AI” OR “Machine Learning” OR “Deep Learning” OR “Radiomics”) AND (“Response Prediction” OR “Response to Treatment” OR “Outcome Prediction”) AND (“Toxicity” OR “Side Effects” OR “Treatment Toxicities” OR “Adverse Events”). Results: The search yielded 29 eligible retrospective studies, published between 2020 and 2025. Eight studies addressed early-stage primary lung cancer, highlighting the potential of AI-based models in predicting radiation-induced pneumonitis, fibrosis and local control. Five studies investigated AI models for predicting hepatobiliary toxicity following SBRT for liver tumors. Sixteen studies involved SRS-treated patients with brain metastases or benign intracranial neoplasms (e.g., arteriovenous malformations, vestibular schwannomas, meningiomas), exploring AI algorithms for predicting treatment response and radiation-induced changes. In the results, AI might have been exploited to both reaffirm already known clinical predictors and to identify novel imaging, dosimetric or biological biomarkers. Examples include predicting radiation pneumonitis in lung cancer, residual liver function in hepatic tumors and local recurrence in brain metastases, thus supporting tailored treatment decisions. Conclusions: Combining AI with SBRT could greatly enhance personalized cancer care by predicting patient-specific outcomes and toxicity. AI models analyze complex datasets, including imaging and clinical data, to identify patterns that traditional methods may miss, thus enabling more accurate risk stratification and reducing variability in treatment planning. With further research and clinical validation, this integration could make radiotherapy safer, more effective and contribute to advancement in precision oncology. Full article

Ischemic stroke remains one of the leading causes of mortality and disability, and accurate segmentation of the affected areas on CT brain images plays a crucial role in timely diagnosis and clinical decision-making. This study proposes an ensemble approach based on the combination of the transformer-based models SE-UNETR and Swin UNETR using a weighted voting strategy. Its performance was evaluated using the Dice similarity coefficient, which quantifies the overlap between the predicted lesion regions and the ground-truth annotations. In this study, three-dimensional CT scans of the brain from 98 patients with a confirmed diagnosis of acute ischemic stroke were used. The data were provided by the International Tomography Center, SB RAS. The experimental results demonstrated that the ensemble based on transformer models significantly outperforms each individual model, providing more stable and accurate predictions. The final Dice coefficient reached 0.7983, indicating the high effectiveness of the proposed approach for ischemic lesion segmentation in CT images. The analysis showed more precise delineation of ischemic lesion boundaries and a reduction in segmentation errors. The proposed method can serve as an effective tool in automated stroke diagnosis systems and other applications requiring high-accuracy medical image analysis. Full article

Treatment-resistant schizophrenia (TRS) affects up to one in three individuals with schizophrenia and is associated with a significant clinical, social, and economic burden. Different from treatment-responsive forms, TRS appears to involve other biological mechanisms extending beyond dopaminergic dysfunctions. This review outlines current knowledge on the molecular and cellular basis of TRS, focusing on alterations in glutamate signaling, imbalances between excitatory and inhibitory activity, disruptions in D-amino acid metabolism, and evidence of neuroinflammation, oxidative stress, and mitochondrial or endoplasmic reticulum dysfunction. Data from genomics, proteomics, metabolomics, preclinical models, and postmortem studies suggest that TRS may have a peculiar neurobiological substrate. Further, multimodal brain imaging studies reveal differences in brain structure, white matter integrity, and network connectivity when compared to treatment-responsive individuals. Altogether, these findings support a shift from the traditional dopamine hypothesis toward a more comprehensive model that includes multiple immune, metabolic, and synaptic factors. Understanding the possible interplay of these complex mechanisms may lead to the identification of potential biomarkers that may help to predict antipsychotic response, as well as the development of more targeted treatments. Early recognition and a deeper biological insight into TRS are essential for improving care and guiding personalized therapeutic strategies. Full article

This study aimed to compare the changes in brain functional connectivity between states of stress induction and recovery in mentally stable, healthy individuals to investigate the effects of stress on brain networks. We selected a stable group comprising 20 healthy adults with Perceived Stress Scale scores of 0–13 points and a mean age of 24.4 ± 4.3 years. We used the Montreal Imaging Stress Task to induce stress and captured images of the brain using a 3T magnetic resonance imaging scanner. We analyzed the region of interest (ROI)-to-ROI connectivity and compared the differences in functional connectivity between the stress and recovery phases. In the stress state, we observed increased connectivity between the dorsal attention and sensorimotor networks and between the visual and default mode networks. In the recovery state, the default mode network became reactivated, and connectivity supporting self-referential thinking and stability was observed. The connectivities observed only in the recovery phase were Language.pSTG (R)—DefaultMode.LP (R) and DefaultMode.LP (R)—Visual.Lateral (R). Our findings provide important basic data for the development of stress management and recovery strategies. By assessing healthy individuals, our findings provide new perspectives on stress resilience in the brain. Full article

Early detection of cancer is critical for effective treatment, particularly for aggressive malignancies like skin cancer and brain tumors. This research presents an integrated deep learning approach combining augmentation, segmentation, and classification techniques to identify diverse tumor types in skin lesions and brain MRI scans. Our method employs a fine-tuned InceptionV3 convolutional neural network trained on a multi-modal dataset comprising dermatoscopy images from the Human Against Machine archive and brain MRI scans from the ISIC 2023 repository. To address class imbalance, we implement advanced preprocessing and Generative Adversarial Network (GAN)-based augmentation. The model achieves 97% accuracy in classifying images across ten categories: seven skin cancer types, multiple brain tumor variants, and an “undefined” class. These results suggest clinical applicability for multi-cancer detection. Full article