SEARCH · Search PubMed

Results for “Convolutional Neural Networks”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

119 recordsLinked to original sources

PubMed2026

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (Rp2=0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Plant Leaves↗

PubMed2026

Systematic evaluation of one-dimensional-to-two-dimensional near-infrared spectroscopy transformations with deep learning for quantifying coconut sap adulteration.

Near-infrared (NIR) spectroscopy have limitations when combined with deep learning (DL) algorithms because they rely on low-dimensional datasets. Therefore, we investigated the potential of transforming one-dimensional (1D) NIR spectra into two-dimensional (2D) spectrograms using synchronous and asynchronous techniques and the continuous wavelet transform (CWT) and their effectiveness by integrating with DL for detecting adulteration in coconut sap. NIR spectra (12,500-4000 cm-1) were collected from binary mixtures (0%-100%;w/w). The performance of all DL (convolutional neural networks-CNN, AlexNet and ResNet) models was compared with that of partial least squares (PLS). The models were ranked in the mentioned order based on their performances: 2D-CWT > 2D-asynchronous > 2D-synchronous > 1D/2D-PLS. The important features of the best model can be explained and visualized using gradient-weighted-class-activation-mapping. The findings highlight that the 1D-to-2D NIR data transformation combined with DL is a highly robust approach because it addresses the feature representation gap in NIR data and effectively captures the spatial-spectral correlations.

Spectroscopy, Near-Infrared↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗

PubMed2026

Artificial neural network data fusion-mediated dual-mode sensor based on Fe3O4@PdIr for Salmonellatyphimurium detection in food.

Salmonella Typhimurium (S. typhimurium) is a major foodborne pathogen that poses a serious threat to public health. In this study, a colorimetric/electrochemical dual-mode biosensor assisted by artificial neural network (ANN) was developed for the sensitive detection of S. typhimurium. Fe3O4@PdIr nanocomposites with enhanced peroxidase-like activity and electrochemical performance were prepared and conjugated with an aptamer specific to S. typhimurium to obtain Fe3O4@PdIr-Apt. Through the sandwich binding of Fe3O4@PdIr-Apt and Apt to the target, the nanocomposites were attached to microplates or Au electrodes, thereby generating colorimetric and electrochemical signals. The ANN model deeply resolved the complex nonlinear relationship between the dual signals, enabling mutual correction and ultimately performing data fusion to output a single detection result, which significantly reduced the mean square error while improving detection sensitivity and reliability. This sensor exhibited a wide linear range of 2.7-2.7 × 108 CFU/mL and a low detection limit of 1.66 CFU/mL. Additionally, this method was successfully applied to the detection of S. typhimurium in pork and milk, with a recovery rate of 95.19% ∼ 104.07%. It indicated that the constructed sensor holds great practical potential for S. typhimurium detection.

Neural Networks, Computer↗

PubMed2026

Ultra-high-field 7T MRI reveals neural abnormalities of attention networks in relation to cognitive impairment in hypertension.

Hypertension is a significant risk factor for cognitive impairment (CI), yet the corresponding neural network abnormalities remain underexplored. In this study, we examined the associations among global and domain-specific cognitive dysfunction, neuroimaging measures, and blood pressure in a subgroup of hypertensive patients with CI (N = 41) from a randomized controlled trial who underwent ultra-high-field 7 T MRI. Structural atrophy related to CI was localized to regions overlapping the attention networks. Both whole-brain and within-network dysfunction of the attention networks were associated with worse global cognitive performance. Notably, hyperconnectivity within key attention network hubs, including the right anterior insula and posterior intraparietal sulcus, was associated with declined processing speed in hypertensive patients, mediating the association between pulse pressure and processing speed. These findings provide new insights into the neural pathophysiology of hypertension-related CI and suggest potential network-based targets for intervention.

Magnetic Resonance Imaging↗

PubMed2026

The landscape of pruning for large language models: A systematic review and unified taxonomy.

Confronting the inherent tension between the exceptional capabilities and the immense computational costs of Large Language Models (LLMs), pruning has become a crucial technique for achieving efficient deployment. However, a systematic analytical framework dedicated specifically to LLM pruning remains absent. In this paper, we aim to bridge this gap. We first elucidate the theoretical foundations that underpin the effectiveness of pruning, namely overparameterization and redundancy, and then propose a multidimensional taxonomy that organizes existing approaches along the axes of granularity, timing, and criteria. Building upon this unified perspective, we further analyze performance recovery mechanisms and the broader evaluation ecosystem, while also exploring forward-looking challenges such as interpretability, automation, and hardware-algorithm co-design. Through this comprehensive synthesis, we seek to provide an integrated and coherent analytical lens for advancing both research and practice in LLM pruning.

Large Language Models↗

PubMed2026

Targeting cortico-striatal-amygdalar networks via theta-band frontoparietal synchronization in opioid use disorder: a randomized tACS-fMRI Trial.

Theta-band oscillation is integral to fronto-parietal connectivity in the executive control network and its top-down regulation on subcortical areas. External frontoparietal synchronization using theta-frequency transcranial alternating current (tACS) is a technology to potentially engage this network. In this pre-registered, triple-blind, sham-controlled trial (NCT03907644), we tested this intervention targeting the right frontoparietal network in people with opioid use disorder (OUD) to measure network engagement and behavioral outcomes. Sixty male participants with OUD were randomized to receive 20 min of active or sham 6 Hz tACS (HD electrodes over F4 and P4). Structural, resting-state, task-based fMRI drug cue reactivity, and repeated cue-induced craving assessments were collected immediately before and after stimulation. Pre-registered outcome measures were analyzed using time × group interaction models to examine (1) modulation of drug cue-related brain activity, (2) changes in craving, (3) alterations in functional connectivity, and (4) relationship between electric field, neural responses, and craving behavior. (1) A significant Time × Group interaction revealed decreased post-stimulation opioid cue-related activity in the active group relative to sham, involving key nodes in reward processing (ventral striatum, amygdala and ventral tegmental area) (FWE corrected α = 0.05) (2) subjective craving did not differ significantly between groups (3) Group by time generalized psychophysiological interaction analyses showed increased right frontoparietal network engagement (β = 2.63, p= 0.0308) following stimulation, and increased top-down inhibitory regulation of frontoparietal network on right ventral striatum (β = 1.99, p= 0.037) and left medial amygdala (β = 1.97, p= 0.039) (4) Electric field strength in the right frontal/parietal node predicted frontoparietal network engagement in the active group (r = 0.43, p= 0.02). Together, these findings demonstrate that theta-band frontoparietal tACS can modulate activity and task-dependent coupling within cortical-subcortical circuits in OUD, supporting network-targeted neuromodulation as a potential intervention for addiction.

Humans↗

PubMed2026

ORBIT: Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space for cancer driver gene identification.

Accurate identification of cancer driver genes is crucial for precision oncology but remains challenging due to the complexity of integrating heterogeneous data and modeling dynamic biological systems. To address these limitations, we propose ORBIT (Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space). Our framework synergistically fuses multi-omics profiles with functional network data using a context-adaptive graph reweighting mechanism to capture cancer-specific dynamics. The model employs a bi-prototype contrastive learning strategy within hyperbolic space, which aligns gene representations around distinct driver and non-driver semantic anchors while preserving the intrinsic hierarchy of biological networks. Comprehensive evaluations demonstrate that ORBIT achieves highly competitive stability in pan-cancer analysis while consistently outperforming state-of-the-art methods in cancer-specific predictions. Furthermore, functional enrichment analysis confirms that the model effectively segregates core cancer pathways, and drug sensitivity profiling validates the clinical relevance of the identified drivers. By integrating hyperbolic geometry with context-adaptive learning, ORBIT offers a robust and interpretable paradigm for precision medicine. The source codes and datasets are publicly accessible at https://github.com/spcho-dev/ORBIT.

Humans↗

PubMed2026

Continuous theta-burst stimulation over the right DLPFC modulates central executive network connectivity in depression: exploratory analysis of a randomized clinical trial.

Previous studies suggest that transcranial magnetic stimulation exerts antidepressant effects and is associated with alterations in functional connectivity (FC), but the neural correlates remain unclear. This exploratory sham-controlled trial investigated the effect of continuous theta-burst stimulation (cTBS) over the right dorsolateral prefrontal cortex (DLPFC) on FC in major depressive disorder (MDD). Seventy MDD patients were randomized to receive two-week treatment of personalized cTBS or sham stimulation. Resting-state fMRI was performed at baseline and post-treatment. Ultimately, 31 patients in the active cTBS group and 28 patients in the sham group passed imaging quality control and were included in the final analysis. To identify the FC that may have been influenced by cTBS treatment, two complementary FC analyses were conducted: (1) voxel-wise degree centrality (DC) followed by seed-based FC, and (2) an individual FC analysis based on the stimulation targets. Furthermore, correlations between FC changes and clinical symptoms improvement were examined. Both groups exhibited reductions of depression scores, with greater improvement in the active group. Compared to the sham group, active cTBS showed increased DC in the precuneus and elevated FC between the precuneus (within the para-cingulate network) and the right inferior parietal lobule (IPL) and DLPFC. Further stimulation target-based analysis revealed increased FC between stimulation targets and both the precuneus and visual regions following treatment. Our findings reveal neural changes associated with cTBS over the right DLPFC in MDD, notably involving the precuneus and its connectivity with the right IPL/DLPFC, suggesting alterations within the central executive network. TRIAL REGISTRATION: chictr.org.cn; ChiCTR2300068273.

Humans↗

PubMed2026

Dissociable neural mechanisms of cognitive enhancement through transcranial stimulation and behavioral training.

BACKGROUND: Transcranial direct current stimulation (tDCS) and adaptive working memory (WM) training are promising cognitive enhancement approaches; however, their neural mechanisms and potential synergies remain poorly understood. OBJECTIVE: We directly compared how tDCS and WM training modulate neural oscillations during WM performance and examined whether combining both interventions produces additive effects. METHODS: We randomized 112 healthy adults into four groups: control (sham tDCS + non-adaptive 1-back), tDCS-only (active tDCS + non-adaptive 1-back), training-only (sham tDCS + adaptive n-back training), or combined (active tDCS + adaptive training). Participants underwent five daily intervention sessions. We recorded high-density EEG during transfer n-back tasks at baseline, post-intervention, and one-week follow-up. RESULTS: All active interventions improved WM performance relative to the control group, with the combined group showing the largest gains (n-back accuracy: +15.6% vs. + 10.1% tDCS-only, +9.7% training-only, +0.7% control; all p < 0.001). Critically, tDCS selectively increased gamma-band (30-50 Hz) power in the frontal and parietal regions (cluster p = 0.018, d > 1.0), whereas WM training enhanced frontal theta-band (4-8 Hz) power and theta-gamma phase-amplitude coupling (both cluster p < 0.012, d > 0.85). The combined group exhibited both neural signatures. Brain-behavior correlations revealed dissociable relationships: gamma increases predicted n-back accuracy improvements (r = 0.61, p < 0.001), whereas theta enhancements correlated with operation span gains (r = 0.58, p = 0.002). CONCLUSIONS: tDCS and WM training enhance cognition through distinct yet complementary neural mechanisms: tDCS via gamma-mediated cortical excitability and WM training via theta-mediated cognitive control. These findings provide neurophysiological evidence for multimodal enhancement strategies that target parallel pathways within WM networks.

Humans↗

PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans↗

PubMed2026

Dual-tasking reveals severity-dependent reorganization of cortical beta energy landscapes in Parkinson's disease.

Dual-task impairment is a hallmark of Parkinson's disease (PD), yet the large-scale neural mechanisms underlying postural-motor interference remain poorly understood. In particular, it is unclear how cortical network dynamics reorganize across disease severity when postural control competes with concurrent task demands. This study investigated EEG-derived beta-band cortical energy landscapes in healthy older adults, early-stage PD, and mid-stage PD during single- and dual-task conditions. Dual-task behavioral cost increased with disease severity for concurrent manual performance (p < 0.001), whereas a quadratic pattern was observed for postural performance. Energy landscape analysis revealed severity-dependent reconfiguration of cortical beta dynamics. Dual-task-related landscape changes in effective network flexibility (ΔNeff), landscape geometry (ΔEvar and ΔGmag), and dominant low-energy attractor organization (ΔLow mass and ΔLow area) showed significant monotonic trends (p < 0.05), reflecting progressive constrained cortical network dynamics with advancing PD severity. In addition, dual-task-related landscape alterations were associated with clinical severity, as indexed by Hoehn and Yahr stage (|r| = 0.353-0.423, p = 0.016-0.048), and showed associations with motor impairment, as measured by MDS-UPDRS part III scores (|r| = 0.333-0.455, p = 0.009-0.063). These findings demonstrate that dual-task demands induce severity-dependent reconfiguration of cortical beta energy landscapes in PD. Energy landscape geometry may capture systems-level neural constraints associated with dual-task susceptibility in PD, providing a physiologically grounded framework to characterize disease-related functional vulnerability.

Humans↗

PubMed2026

Brain network alterations underlying cue reactivity and craving in abstinent methamphetamine users: a systematic review of functional MRI findings.

BACKGROUND: Methamphetamine use disorder (MUD) is marked by intense craving and high relapse risk, often triggered by drug-related cues. Functional magnetic resonance imaging (fMRI) provides key insight into the neural basis of this cue reactivity, implicating large-scale brain networks for reward, motivation, and control. Yet, findings remain inconsistent across studies due to differences in task design, abstinence duration, and participant characteristics. OBJECTIVE: This systematic review synthesises evidence on how abstinence influences brain network alterations underlying cue reactivity and craving in methamphetamine users, integrating task-based and resting-state fMRI findings within leading neurobiological models of addiction. METHODS: A systematic search of PubMed, Scopus, Web of Science, and Ovid was conducted up to August 10, 2025, following PRISMA 2020 guidelines. Eligible fMRI studies examined cue reactivity or craving in abstinent methamphetamine users. Data were extracted on activation, connectivity, and brain-behaviour associations, and synthesised narratively. RESULTS: Task-based studies revealed heightened activation across reward, salience, and control networks during cue exposure, which diminished as parietal and executive control systems re-engaged with longer abstinence. Resting-state findings showed disrupted intrinsic connectivity among default mode, salience, and frontoparietal networks, reflecting persistent imbalances linked to craving and use severity. CONCLUSION: fMRI evidence shows that MUD is marked by network-level disruption linking reward, salience, and control systems. Task-based findings reveal strong cue reactivity in reward circuits, while resting-state data show persistent imbalance among default mode and control networks. With abstinence, partial restoration of network integrity emerges, highlighting both vulnerability and opportunities for targeted, recovery-based interventions.

Humans↗

PubMed2026

Neurocorrelates of nocturnal enuresis in pre-adolescent children.

INTRODUCTION: Nocturnal enuresis (NE) is a common neurodevelopmental condition, yet its underlying neural mechanisms remain unclear. This study leverages the large-scale Adolescent Brain Cognitive Development (ABCD) dataset to identify structural and functional brain correlates associated with active symptoms and the resolution of bedwetting. METHODS: Using cross-sectional data from 3472 participants aged 9-10 years, children were categorized into three groups: active nocturnal enuresis (ANE, n = 225), history of nocturnal enuresis (HNE, n = 1171), and healthy control groups (CG, n = 2076). Multimodal neuroimaging protocol evaluated macrostructural properties via structural MRI (sMRI), microstructural white matter integrity via diffusion MRI (dMRI), and functional connectivity via resting-state fMRI (fMRI). Group differences were evaluated using linear models within an ANCOVA framework, adjusting for intracranial volume and handedness with False Discovery Rate (FDR) correction. RESULTS: Compared to controls, the ANE group exhibited a significant volume deficit in the right caudate, decreased sulcal depth in the left insula, and lower internal correlation within the Cingulo-Opercular Network (CON). Conversely, the dry HNE group demonstrated significant structural adaptations, including bilaterally larger putamen volumes and increased right caudate volume compared to the ANE group. The HNE group also showed increased microstructural density (decreased mean diffusivity) in the bilateral hippocampus and an increased cortical surface area in the left insula. Both NE groups demonstrated persistently reduced functional coupling within the CON. CONCLUSIONS: Nocturnal enuresis appears to be associated with a potential complex central signaling deficits. Reduced internal correlation within the CON across both active and former bedwetters indicates a potential for impairment in processing internal homeostatic bladder signals during sleep.

Humans↗

PubMed2026

Disentangling oscillatory and aperiodic neural activity in autism: A spectral parameterization analysis of neurofeedback intervention.

BACKGROUND: Autism Spectrum Disorder (ASD) is characterized by atypical neural oscillations and heterogeneous alterations in excitation/inhibition (E/I) balance, the directionality of which varies across individuals, neural circuits, and developmental stages. While Alpha-band neurofeedback (NFB) is a promising intervention, its underlying neurophysiological mechanisms remain unclear, partly due to the conflation of periodic and aperiodic signals in traditional EEG analysis. METHODS: This randomized controlled trial recruited 40 children with ASD, assigned to either an experimental group (Alpha-training NFB) or a no-feedback group. Resting-state EEG and behavioral assessments (SRS, ABC) were collected pre- and post-intervention. We employed spectral parameterization to decompose neural activity into aperiodic (1/f slope, offset) and periodic (periodic alpha power, center frequency) components. RESULTS: NFB training yielded significant behavioral improvements in social cognition and relating skills. Physiologically, the experimental group exhibited a significant steepening of the aperiodic slope (increased exponent), reflecting a reduction in neural noise and potential optimization of inhibitory modulation. Furthermore, we observed enhanced periodic alpha power and an acceleration of the alpha center frequency (ACF), indicative of improved neural efficiency and maturation. These physiological shifts in frontal and occipital regions were significantly correlated with improvements in behavioral scores. CONCLUSION: Alpha-training NFB was associated with improvements in caregiver-rated behavioral scores and modulated spectral features of resting-state EEG in children with ASD. These findings validate the utility of spectral parameterization markers in evaluating neuromodulatory interventions.

Humans↗

PubMed2026

Machine learning-assisted Mn-N-C nanozyme colorimetric sensor array for trace-level detection of biogenic amines in meat.

Accurate detection of biogenic amines (BAs) in meat remains challenging due to their high structural similarity and co-occurrence. Herein, an Mn-N-C nanozyme was synthesized via a metal-organic framework confined pyrolysis strategy, possessing excellent oxidase (OXD)- and peroxidase (POD)-like activities. The dual enzyme-like activity showed Km values of 0.1584 mM (OXD) and 0.1498 mM (POD), respectively, in detection system. Leveraging these properties, a colorimetric sensor array was constructed, enabling the detection of four representative BAs within a concentration range of 2-10 ppm with 100% classification accuracy. In addition, a concentration independent recognition model based on an artificial neural network was developed to address signal nonlinearity interference in meat. The integrated system achieved accurate trace-level identification of BAs in perishable fish, pork, and chicken, demonstrating its applicability for early-stage BAs monitoring and quality deterioration warning during storage and transportation.

Biogenic Amines↗

PubMed2026

Dissociating behavioral, neural and experiential effects of prefrontal HD-tDCS during conflict resolution.

Inconsistent evidence regarding the cognitive effects of transcranial direct current stimulation (tDCS) highlights the need for more comprehensive approaches to assess its impact. This study aimed to investigate the effects of high-definition tDCS (HD-tDCS) on conflict resolution by combining behavioral, neural, and subjective experience measures. Sixty participants were randomly assigned to anodal, cathodal, or sham HD-tDCS groups and completed a 30-min flanker task. EEG was recorded during the first and last blocks (without stimulation), while stimulation was applied during the intermediate blocks of the task. Using a multidimensional methodological approach including Drift-Diffusion Modeling (DDM), EEG spectral analysis, Lempel-Ziv complexity, and Temporal Experience Tracing (TET), we assessed the cognitive, neural, and phenomenological effects of stimulation. Behavioral results indicated no significant improvements in reaction times or accuracy across the stimulation groups. Similarly, DDM parameters showed no effect of HD-tDCS on latent cognitive processes. However, EEG data revealed a significant reduction in neural complexity in the anodal group during resting-state, suggesting a stabilization or reorganization of neural dynamics. Subjective experience analysis identified two distinct clusters of task-related feelings, though time spent in these experiential states did not differ between groups. Interestingly, sensation of stimulation was significantly higher for anodal stimulation than sham when analyzed as a single dimension. Despite null behavioral effects, this study provides important insights into the neural and subjective responses to HD-tDCS and highlights the value of integrating complementary multidimensional approaches to better characterize brain stimulation effects. These findings contribute to the ongoing debate about the efficacy of tDCS in cognitive enhancement.

Humans↗

PubMed2026

Clinical efficacy and brain mechanism characteristics of guide chi and regulate spirit tuina therapy in the treatment of post-stroke walking dysfunction: A randomized controlled trial based on fNIRS.

BACKGROUND: This study aims to preliminarily evaluate the role of Guide Chi and Regulate Spirit(GCRS) Tuina in enhancing walking function in post-stroke patients with walking dysfunction; secondly, by using functional near-infrared spectroscopy (fNIRS), it investigates the effect of GCRS Tuina on the restoration of brain function in this patient population. METHODS: Participants in the control group received 4-week rehabilitation treatment, while those in the Combined Tuina Group (CTG) additionally received GCRS Tuina therapy for another 4 weeks on this basis. Functional Ambulation Category (FAC), Fugl - Meyer Assessment Scale for Lower Extremity Motor Function (FMA - LE), and Modified Barthel Index (MBI) were evaluated at the baseline and after 4 treatment weeks. A gait and motion analysis system was used to measure step length, stride, walking speed, and step frequency. FNIRS was used to measure the resting-state functional connectivity(FC) strength, as well as the β - value and HbO2 concentration mean during the walking task. RESULTS: A total of 60 participants completed the randomized, and 53 completed the trial and entered the statistical analysis. Compared with the Single Rehabilitation Group(SRG), the CTG group had higher FAC, FMA-LE, and MBI scores after 4 weeks. After the treatment course, the standardized step length, stride, walking speed, and step frequency of the CTG were higher than SRG. At the Region of Interest(ROI) level, the CTG exhibited 13 inter-ROI FC strengths that were higher than SRG, and the differences could survive the FDR correction (PFDR<0.05). Under the walking task, the CTG group had higher β values in 18 channels and higher Oxyhemoglobin(HbO2) concentrations in 19 channels than the SRG (PFDR <0.05). CONCLUSION: GCRS Tuina therapy can significantly improve patients' walking function, enhance lower limb motor ability and daily living ability. It can improve walking efficiency. Tuina can significantly increase the FC and enhance the activation levels and HbO2 concentrations. The stimulation of Tuina may help reconstruct the brain's motor control network, restore impaired motor function, promote the occurrence of neural plasticity, strengthen the neural circuits in the cognitive-motor-sensory cortex to improve walking function. TRIAL REGISTRATION: This study has been registered with the International Traditional Medicine Clinical Trial Registry (ITMCTR2024000654).

Humans↗