Search Results (16,519)

The development of efficient bioelectrodes requires suitable fabrication strategies, starting with the electrode material, which affects the electron transfer between the biocatalyst and the electrode surface. Then, selection and adjustment of the enzyme immobilization conditions are essential to enhance the performance of the bioelectrodes for their desirable utility. In this study, we report the fabrication of a high-performance bioelectrode using a one-step crosslinking of FAD-dependent glucose dehydrogenase (FAD-GDH) and thionine acetate as a redox mediator, with genipin serving as a natural, biocompatible crosslinker. Electrodes were manufactured on flexible polyester substrates using a direct printing technique, enabling reproducible and low-cost production. Among the tested crosslinkers, genipin significantly enhanced the catalytic performance of bioelectrodes. Comparative studies on graphite, silver, and gold electrode materials identified graphite as the most suitable due to its extended electroactive surface area. The developed bioelectrodes applied to glucose biosensing demonstrated a linear amperometric response to glucose in the range of 0.02–2 mM and 0.048–30 mM, covering clinically relevant concentrations. The application of artificial sweat confirmed high detection accuracy. These findings highlight the potential integration of genipin-based enzyme–mediator networks for future non-invasive sweat glucose monitoring platforms. Full article

Background/Objectives: Inter-individual variability in injury severity represents a major barrier to reproducibility in neonatal hypoxia–ischemia (HI) models. Objective early postoperative stratification of animals is therefore essential for standardized group allocation and reliable assessment of experimental outcomes. This study aimed to evaluate whether laser speckle contrast imaging (LSCI) can be used as a rapid, noninvasive tool for early post hoc stratification of ischemic brain damage severity in neonatal mice following HI. Methods: Neonatal CD-1 mice (postnatal day 9; n = 60) underwent hypoxia–ischemia using a modified Rice–Vannucci protocol. Cerebral perfusion was assessed by laser speckle contrast imaging at baseline, 3 h, and 7 days after HI. The difference in mean perfusion between ipsilateral and contralateral hemispheres at 3 h (Δ perfusion) was used to stratify animals into severity groups. Brain injury was quantified by 2,3,5-triphenyltetrazolium chloride (TTC) staining at 24 h and 7 days. Survival was monitored for 7 days and analyzed using Kaplan–Meier curves and the log-rank (Mantel–Cox) test. Results: LSCI-derived Δ perfusion at 3 h enabled the formation of distinct injury-severity groups (no visible damage, mild, moderate, and severe) with significant between-group differences (p < 0.0001). TTC-based lesion area increased stepwise across severity groups, and Δ perfusion correlated with lesion size when all animals were analyzed together (r = 0.688, p = 0.0011). No significant correlations were observed within individual severity groups, indicating that the overall association was driven primarily by between-group differences. Survival analysis revealed 75% mortality in the severe injury group (p < 0.0001). Conclusions: LSCI represents a robust and practical approach for early, objective, group-level stratification of neonatal mice by HI injury severity, thereby improving reproducibility and statistical validity in preclinical studies. However, its ability to predict outcomes within individual severity categories is limited, and repeated long-term measurements may pose technical challenges. Full article

Background: Head and neck squamous cell carcinoma (HNSCC) remains the seventh most common cancer worldwide, characterized by late-stage diagnosis and poor 5-year survival rates. Oral squamous cell carcinoma (OSCC) is the most prevalent subtype. The identification of robust diagnostic, prognostic, and predictive markers is essential for personalized treatment monitoring. Methods: Following PRISMA and PICO standards, we conducted a comprehensive review of studies published over the past 10 years across PubMed/MEDLINE, Scopus, and Web of Science. The selection process was facilitated by AI-powered tools (Rayyan QCRI), and study quality was assessed using NOS or QUIPS. Results: 34 articles (including meta-analyses and original trials) were identified. Established clinical markers, such as p16-positivity (HR ≈ 0.55) and PD-L1 (CPS), remain significant. However, the molecular landscape is expanding to include high-risk lncRNA signatures (HR ≈ 2.50), immune checkpoints such as TIGIT (HR ≈ 1.85), and genomic alterations, including IL-10 promoter polymorphisms. We highlight that epigenetic silencing of p16 affects only about 25% of patients, while metabolic regulators (e.g., GLUT-1) and protein markers (e.g., MASPIN) offer critical predictive value for therapy response. Conclusions: The diagnostic and predictive paradigm is shifting toward a multi-omic approach that integrates DNA, RNA, proteins, and metabolic indicators. Future clinical use will rely on AI-driven multimarker panels and non-invasive liquid biopsies to enable real-time monitoring and de-escalation of treatment strategies. Full article

Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique that utilizes magnetic fields to induce cortical electric currents, enabling both the measurement and modulation of neuronal activity. Initially developed as a diagnostic tool, TMS now serves dual roles in clinical neurology, offering insight into neurophysiological dysfunctions and the therapeutic modulation of abnormal cortical excitability. This review examines key TMS outcome measures, including motor thresholds (MT), input–output (I/O) curves, cortical silent periods (CSP), and paired-pulse paradigms such as short-interval intracortical inhibition (SICI), short-interval intracortical facilitation (SICF), intracortical facilitation (ICF), long interval cortical inhibition (LICI), interhemispheric inhibition (IHI), and short-latency afferent inhibition (SAI). These biomarkers reflect underlying neurotransmitter systems and can aid in differentiating neurological conditions. Diagnostic applications of TMS are explored in Parkinson’s disease (PD), dystonia, essential tremor (ET), Alzheimer’s disease (AD), and mild cognitive impairment (MCI). Each condition displays characteristic neurophysiological profiles, highlighting the potential for TMS-derived biomarkers in early or differential diagnosis. Therapeutically, repetitive TMS (rTMS) has shown promise in modulating cortical circuits and improving motor and cognitive symptoms. High- and low-frequency stimulation protocols have demonstrated efficacy in PD, dystonia, ET, AD, and MCI, targeting the specific cortical regions implicated in each disorder. Moreover, the successful application of TMS in differentiating and treating AD and MCI underscores its clinical utility and translational potential across all neurodegenerative conditions. As research advances, increased attention and investment in TMS could facilitate similar diagnostic and therapeutic breakthroughs for other neurological disorders that currently lack robust tools for early detection and effective intervention. Moreover, this review also aims to underscore the importance of maintaining standardized TMS protocols. By highlighting inconsistencies and variability in outcomes across studies, we emphasize that careful methodological design is critical for ensuring the reproducibility, comparability, and reliable interpretation of TMS findings. In summary, this review emphasizes the value of TMS as a distinctive, non-invasive approach to probing brain function and highlights its considerable promise as both a diagnostic and therapeutic modality in neurology—roles that are often considered separately. Full article

Background and Objectives: Acute respiratory failure (ARF) has a heterogeneous course in the emergency department (ED), and early prediction of noninvasive mechanical ventilation (NIMV) failure is difficult. The PaCO₂–ETCO₂ gap reflects ventilation–perfusion mismatch and increased physiologic dead space; however, the prognostic value of its short-term change during NIMV is unclear. This study evaluated baseline, post-treatment, and delta (post–pre) PaCO₂–ETCO₂ gap values for predicting intubation, intensive care unit (ICU) admission, and mortality in ED patients with ARF receiving NIMV. Materials and Methods: This prospective observational study enrolled adults (≥18 years) treated with NIMV in a tertiary ED. Exclusion criteria included GCS < 15, intoxication, pneumothorax, trauma, pregnancy, gastrointestinal bleeding, need for immediate intubation/CPR, or incomplete data. ETCO₂ was recorded within the first 3 min of NIMV and at 30 min; concurrent arterial blood gases provided PaCO₂. The PaCO₂–ETCO₂ gap was calculated at both time points and as delta. Outcomes were intubation, ICU admission, and mortality. ROC analyses determined discriminatory performance and cutoffs using the Youden index. Results: Thirty-four patients were included (50% female; mean age 73.26 ± 10.07 years). Intubation occurred in 9 (26.5%), ICU admission in 20 (58.8%), and mortality in 10 (29.4%). The post-treatment gap and delta gap were significantly higher in intubated patients (p = 0.007 and p = 0.001). For predicting intubation, post-treatment gap > 10.90 mmHg yielded AUC 0.807 (p = 0.007; sensitivity 77.8%, specificity 76.0), while delta gap > 2.90 mmHg yielded AUC 0.982 (p = 0.001; sensitivity 88.9%, specificity 92.0). Delta gap also predicted ICU admission (cutoff > 0.65 mmHg; AUC 0.746, p = 0.016) and mortality (cutoff > 2.90 mmHg; AUC 0.865, p = 0.001). Conclusions: In ED ARF patients receiving NIMV, an increasing PaCO₂–ETCO₂ gap—especially the delta gap—was associated with higher risks of intubation, ICU admission, and mortality, supporting serial CO₂ gap monitoring as a practical early warning marker of deterioration. Full article

Background: Repetitive peripheral magnetic stimulation (rPMS) has emerged as a promising non-invasive treatment modality for reducing muscle hypertonus and spasticity. However, standardized protocols regarding stimulation parameters, particularly the number of stimuli required to achieve therapeutic effects, remain largely undefined. Methods: In an exploratory study, seventeen healthy participants (15 male, 2 female) underwent progressive rPMS treatments at 5 Hz frequency with incrementally increasing stimulus counts (105, 210, 315, 420, and 840 stimuli). Seventeen participants served as controls (11 male, 6 female) receiving sham stimulation. Achilles tendon reflexes were elicited using a computer-controlled reflex hammer, and compound muscle action potential (CMAP) peak-to-peak amplitudes were recorded via surface electromyography before and immediately after each stimulation session. Results: The overall repeated-measures ANOVA indicated a significant main effect (F(5, 80) = 4.98, p = 0.001, η²_p = 0.237). All rPMS treatments produced significant reductions in CMAP amplitudes compared to baseline (p < 0.05). No progressive dose-dependent relationship was observed between stimulus count and response magnitude, suggesting a threshold effect rather than progressive inhibition. Control group showed no significant changes (p ≤ 0.56). Conclusions: Low-frequency (5 Hz) rPMS produces rapid inhibitory effects on spinal reflex circuits with onset after as few as 105 stimuli. These findings indicate that treatment effects can be achieved with substantially fewer stimuli than previously assumed. Further research is needed to identify parameters capable of achieving greater reflex suppression. Full article

Biophotons are non-thermal and non-bioluminescent ultraweak photon emissions, first hypothesised by Gurwitsch as a regulatory mechanism in cell division, and then experimentally observed in living organisms. Today, two main hypotheses explain their origin: stochastic decay of excited molecules and coherent electromagnetic fields produced in biochemical processes. Recent interest focuses on the role of biophotons in cellular communication and disease monitoring. This study presents the first campaign of biophoton emission measurements from cultured astrocytes and glioblastoma cells, conducted at Fondazione Pisana per la Scienza (FPS) using two ultra-sensitive setups developed in collaboration between the National Laboratories of Frascati (LNF-INFN) and the University of Rome II Tor Vergata. The statistical analyses of the collected data revealed a clear separation between cellular signals and dark noise, confirming the high sensitivity of the apparatus. The Diffusion Entropy Analysis (DEA) was applied to the data to uncover dynamic patterns, revealing anomalous diffusion and long-range memory effects that may be related to intercellular signaling and cellular communication. These findings support the hypothesis that biophoton emissions encode rich information beyond intensity, reflecting metabolic and pathological states. The differences revealed by applying the Diffusion Entropy Analysis to the biophotonic signals of Astrocytes and Glioblastoma are highlighted and discussed in the paper. This work lays the groundwork for future studies on neuronal cultures and proposes biophoton dynamics as a promising tool for non-invasive diagnostics and the study of cellular communication. Full article

Background: The vestibular schwannoma (VS) is the most common cerebellopontine angle tumor in adults, exhibiting a highly variable natural history, from stability to rapid growth. Accurate, the non-invasive prediction of tumor behavior is essential to guide personalized management and avoid overtreatment or delayed intervention. Objective: To systematically review and synthesize the evidence on MRI-based biomarkers for predicting VS growth and treatment responses. Methods: We conducted a PRISMA-compliant search of PubMed, EMBASE, and Cochrane databases for studies published between 1 January 2000 and 1 January 2025, addressing MRI predictors of VS growth. Cohort studies evaluating texture features, signal intensity ratios, perfusion parameters, and apparent diffusion coefficient (ADC) metrics were included. Study quality was assessed using the NOS (Newcastle–Ottawa Scale) score, GRADE (Grading of Recommendations, Assessment, Development and Evaluation), and ROBIS (Risk of Bias in Systematic reviews) tool. Data on diagnostic performance, including the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, and p value, were extracted and descriptively analyzed. Results: Ten cohort studies (five retrospective, five prospective, total n = 525 patients) met the inclusion criteria. Texture analysis metrics, such as kurtosis and gray-level co-occurrence matrix (GLCM) features, yielded AUCs of 0.65–0.99 for predicting volumetric or linear growth thresholds. Signal intensity ratios on gadolinium-enhanced T1-weighted images for tumor/temporalis muscle achieved a 100% sensitivity and 93.75% specificity. Perfusion MRI parameters (K^trans, v_e, ASL, and DSC derived blood-flow metrics) differentiated growing from stable tumors with AUCs up to 0.85. ADC changes post-gamma knife surgery predicted a favorable response, though the baseline ADC had limited value for natural growth prediction. The heterogeneity in growth definitions, MRI protocols, and retrospective designs remains a key limitation. Conclusions: MRI-based biomarkers may provide exploratory signals associated with VS growth and treatment responses. However, substantial heterogeneity in growth definitions and MRI protocols, small single-center cohorts, and the absence of external validation currently limit clinical implementation. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by dopaminergic neuronal loss and abnormal α-synuclein aggregation. Circulating microRNAs (miRNAs) have emerged as promising biomarkers and potential modulators of PD-related molecular pathways. In this study, we investigated the expression levels of four candidate miRNAs—miR-15a-5p, miR-16-5p, miR-139-5p, and miR-34a-3p—in patients with PD compared with healthy controls. A total of 47 PD patients and 45 age- and sex-matched controls were enrolled. Plasma miRNA levels were quantified using standardized RNA extraction, cDNA synthesis, and qPCR protocols. We observed marked upregulation of miR-15a-5p and robust downregulation of both miR-139-5p and miR-34a-3p in PD patients, whereas miR-16-5p showed no significant difference between groups. Target gene prediction and functional enrichment analysis identified 432 unique genes, with enrichment in biological processes related to protein ubiquitination and catabolic pathways, and signaling cascades such as mTOR, PI3K-Akt, MAPK, and Hippo pathways, all of which are implicated in neurodegeneration. Elevated miR-15a-5p may contribute to pro-apoptotic mechanisms, while reduced miR-139-5p and miR-34a-3p expression may reflect impaired mitochondrial function, diminished neuroprotection, or compensatory regulatory responses. Together, these dysregulated circulating miRNAs provide novel insight into PD pathophysiology and highlight their potential as accessible, non-invasive biomarkers. Further longitudinal studies in larger and more diverse cohorts are warranted to validate their diagnostic and prognostic value and to explore their utility as therapeutic targets. Full article

Pregnancies complicated by diabetes, including pregestational and gestational diabetes mellitus, are associated with increased maternal and fetal morbidity. Early identification of at-risk pregnancies is crucial for timely intervention and improved outcomes. Emerging evidence highlights the interplay of genetic predisposition, epigenetic modifications, and non-invasive biomarkers in the early detection of diabetic pregnancies. Genetic factors influencing insulin signaling, glucose metabolism, and pancreatic β-cell function may contribute to susceptibility to gestational hyperglycemia. Concurrently, epigenetic alterations, such as DNA methylation and histone modifications in maternal and placental tissues, have been linked to dysregulated metabolic pathways and adverse pregnancy outcomes. Non-invasive biomarkers, including circulating cell-free DNA and microRNAs in maternal blood, show promise for early diagnosis by offering a safer and more practical alternative to invasive testing. Integrating genetic, epigenetic, and molecular marker data could enhance risk stratification and enable personalized monitoring and management strategies. This review synthesizes current knowledge on the molecular underpinnings of diabetic pregnancies, evaluates the potential of emerging biomarkers for early diagnosis, and discusses the challenges and future perspectives for translating these findings into clinical practice. Understanding these mechanisms may pave the way for precision medicine approaches, ultimately improving maternal and neonatal outcomes in pregnancies affected by diabetes. Full article

Cataracts remain the leading cause of blindness worldwide, and surgery is currently the only effective clinical treatment, as no pharmacological therapy has yet been validated. Here, we explore Fullerenol, a hydroxylated fullerene derivative formulated as eye drops, as a potential nanomedicine for delaying cataract onset and progression. In UVB-induced mouse cataract models, topical Fullerenol preserved the lens transparency and histological structure. In human lens epithelial cells, Fullerenol reduced the oxidative stress, restored the mitochondrial function, alleviated the DNA damage, and suppressed the cellular senescence. RNA sequencing and pathway enrichment analyses further indicated that Fullerenol modulated the oxidative stress- and senescence-associated signaling pathways, including MAPK and TGF-β cascades, while downregulating the p53–CDKN1A (p21) axis. These findings provide new evidence that Fullerenol can mitigate photo-oxidative damage and age-related cellular dysfunction, highlighting its promise as a non-invasive and clinically translatable nanomedicine strategy for cataract management. Full article

Background/Objectives: The Bicêtre score for Vein of Galen Aneurysmal Malformation (VGAM) relies on existing end-organ damage. We hypothesized that transthoracic echocardiography (TTE) could quantify significant systemic steal in clinically stable neonates (Bicêtre score ≥ 12). This study evaluates the Aortic Flow Reversal Ratio (AoFRr) as a tool to measure this steal and predict treatment outcomes. Methods: In a single-center retrospective study of patients with VGAM, the AoFRr (the ratio of the diastolic reversal velocity time integral to the systolic forward volume time integral) was calculated via TTE in the abdominal aorta at the level of the diaphragm before and after endovascular embolization. Over the study period, the cohort underwent a total of 30 endovascular interventions and 49 TTEs. Pre-intervention AoFRr was correlated with the Bicêtre score, and post-intervention changes were analyzed for association with the need for subsequent embolizations. Results: In a cohort of 12 patients with a median Bicêtre score of 18, 83.3% had pre-intervention aortic diastolic flow reversal. The median pre-intervention AoFRr was 0.81, indicating substantial systemic steal despite clinical stability. A post-intervention AoFRr reduction of ≥85% was significantly associated with a lower likelihood of requiring re-intervention (p = 0.0253). Conclusions: The AoFRr quantifies substantial hemodynamic steal in VGAM patients who appear clinically stable by the Bicêtre score. Its reduction following embolization predicts a more favorable clinical course. The AoFRr is a valuable, non-invasive adjunct for risk stratification and may help optimize the timing of endovascular intervention. Full article

Background/Objectives: Cognitive decline and compromised attention control serve as early indicators of neurodysfunction that manifest as broader psychopathological symptoms, yet conventional mental health assessment relies predominantly on subjective self-report measures lacking objectivity and temporal granularity. We propose Neurosense, an AI-driven brain health assessment framework using electroencephalography (EEG) to non-invasively capture neural dynamics. Methods: Our Dual-path Spatio-Temporal Adaptive Gated Encoder (D-STAGE) architecture processes temporal and spatial EEG features in parallel through Transformer-based and graph convolutional pathways, integrating them via adaptive gating mechanisms. We introduce a two-stage paradigm: first training on cognitive task EEG for reaction time prediction to acquire cognitive performance-related representations, then featuring parameter-efficient adapter-based transfer learning to estimate p-factor—a transdiagnostic psychopathology dimension. The adapter-based transfer achieves competitive performance using only 1.7% of parameters required for full fine-tuning. Results: The model achieves effective reaction time prediction from EEG signals. Transfer learning from cognitive tasks to mental health assessment demonstrates that cognitive efficiency representations can be adapted for p-factor prediction, outperforming direct training approaches while maintaining parameter efficiency. Conclusions: The Neurosense framework reveals hierarchical relationships between neural dynamics, cognitive efficiency, and mental health dimensions, establishing foundations for a promising computational framework for mental health assessment applications. Full article

Liver fibrosis, the progressive accumulation of scar tissue resulting from chronic liver disease, is increasingly recognized as a multi-system condition, the effects of which extend beyond the liver, affecting brain health. Dementia, characterized by progressively impaired cognition sufficient to impede daily functioning, is a major global health issue with incompletely defined risk factors and pathogenic precursors. To examine the relationship between liver fibrosis and cognitive outcomes, we conducted a comprehensive PubMed literature search, and human studies published in English were included. Evidence is synthesized on the pathophysiology and clinical significance of liver fibrosis, types of dementia, and studies supporting the association between liver fibrosis and cognitive impairment. Meta-analytic data indicate that liver fibrosis is associated with an approximately 30% increased risk of incident dementia (pooled hazard ratio ~1.3), with progressively higher risks across more advanced fibrosis stages. Putative pathomechanisms, potentially modulated by age and sex, include chronic systemic and neuro-inflammation, insulin resistance, vascular dysfunction, and a perturbed intestinal microbiota–liver–brain axis. Non-invasive liver fibrosis diagnostics, advanced neuroimaging, and biomarkers represent key tools for assessing risk. In conclusion, liver fibrosis is a systemic condition that can affect brain health. Early detection, thorough risk assessment and interventions, such as lifestyle changes, metabolic therapies, and antifibrotic treatments, may help protect neural function. Key research gaps are identified, with suggestions for improving understanding of liver fibrosis’s connection to dementia or cognitive impairment. Full article

This study aimed to develop machine learning models based on conventional MRI features to classify HER2 expression levels in invasive breast cancer and explore their association with disease-free survival (DFS). A total of 678 patients from two centers were included, with Center 1 divided into training and internal test sets and Center 2 serving as an external test set. Random Forest models were trained to distinguish HER2-positive vs. HER2-negative (Task 1) and HER2-low vs. HER2-zero tumors (Task 2) using BI-RADS–based MRI features. SHapley Additive exPlanations were applied to rank feature importance, assist feature selection, and enhance model interpretability. DFS was analyzed using Kaplan–Meier curves and log-rank tests. In Task 1, key features included tumor size, axillary lymph nodes, fibroglandular tissue, peritumoral edema, and multifocal, achieving AUCs of 0.75 and 0.73 in the internal and external test sets, respectively. In Task 2, tumor size, peritumoral edema, and multifocal yielded AUCs of 0.73 and 0.72, respectively. Higher task-specific model scores were associated with shorter DFS in Task 1 (p = 0.037) and longer DFS in Task 2 (p = 0.046). MRI-based machine learning models can noninvasively stratify HER2 expression levels, with potential for prognostic stratification and clinical application. Full article