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PubMed2026

Treatment-related associations of nucleus accumbens connectivity within mesocorticolimbic circuits in depression.

Pharmacological treatment remains a mainstay in managing depression, yet the neural correlates associated with treatment response remain incompletely understood. This study used multimodal neuroimaging to examine nucleus accumbens (NAc)-centered structural and functional alterations associated with fluoxetine and Shugan Jieyu Capsule (SG), a traditional Chinese medicine, in patients with mild-to-moderate depression (MMD). Sixty patients were randomized to an 8-week course of fluoxetine or SG. Depression severity was assessed using the 24-item Hamilton Depression Rating Scale (HAMD-24), and structural and functional MRI scans were acquired at baseline and endpoint. Both treatments were associated with significant symptom improvement. Neuroimaging analyses revealed structural and functional alterations involving the NAc. Changes in NAc-amygdala connectivity showed an exploratory association with symptom improvement in the SG group, whereas changes in NAc-rostral anterior cingulate cortex connectivity were associated with symptom improvement in the fluoxetine group and remained significant after correction for multiple comparisons. In addition, remitters exhibited stronger baseline connectivity between the NAc and ventral tegmental area and between the NAc and middle frontal gyrus compared with non-remitters. These findings suggest that NAc-centered connectivity may be relevant to treatment-related neural changes in depression and may inform future research on imaging-based candidate markers of treatment response and personalized treatment approaches. TRIAL REGISTRATION: The study is registered in https://www.chictr.org.cn/ with a registration number ChiCTR1900024988 (date: 08.06.2019).

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 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

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

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

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

Neuroimaging anxious children and adolescents before and after cognitive behavioral therapy: a systematic review.

OBJECTIVE: This systematic review investigates brain changes in youths with anxiety disorders following cognitive behavioral therapy (CBT) and neural markers that predict CBT responses. METHODS: We conducted a systematic search using the electronic databases PubMed, Web of Science, and ProQuest. The inclusion deadline was set to October 27, 2025. We included fifteen peer-reviewed neuroimaging studies that examined the effects of CBT in youths under 19 years old with a primary clinical diagnosis of an anxiety disorder based on DSM-5 criteria. RESULTS: Although the existing literature is marked by substantial diversity in methods and outcomes, task-related neural response in the anterior cingulate cortex (ACC, 2/8, 25.0%), insula (1/8, 12.5%) increased from pre to post CBT and these changes were further correlated with clinical symptom improvements. Moreover, CBT outcomes were predicted by pre-treatment activity or connectivity in the ACC and amygdala (3/13, 23.0%). A smaller proportion of studies (2/13, 15.3%) found that activity or connectivity in the insula, precuneus/cuneus, postcentral gyrus, and activity or structure in the nucleus accumbens (NAcc) predicted response to CBT. The low consistency of these findings was driven by methodological variability, low reliability of the neural markers, and relatively small sample sizes. CONCLUSIONS: This review highlights promises of neural predictors and outcomes to enhance anxiety disorder treatments in children and adolescents, facilitating future personalized and effective CBT. Beyond this initial promise, the field is hindered by methodological inconsistencies and limited replications. While longitudinal and personalized approaches are important next steps, the central challenge remains: identifying neural markers that are both reliable and robust.

Adolescent↗

PubMed2026

GPR3 in neuro-metabolic-immune-reproductive nexus - a potential therapeutic target for Multi-System diseases.

BACKGROUND: GPR3(G-protein-coupled receptor 3), an orphan G-protein-coupled receptor (GPCR) with constitutive Gs activity, is expressed in the brain, liver, ovary, and other tissues, regulating cell proliferation, differentiation, and apoptosis across the nervous, reproductive, immune, and metabolic systems. This review synthesizes evidence on its integrated signaling and physiological functions to address the lack of a comprehensive multisystem pathophysiology overview. METHODS: A systematic literature search was conducted on PubMed and Web of Science, using keywords such as "GPR3", "GPCR", "neurodegeneration", "metabolism", "immune", "reproduction", "agonist", "inhibitor", and "therapeutic target". This search identified GPR3's roles in neurodegenerative diseases, immune inflammation, reproduction, and energy metabolism. The analysis focused on signaling pathways, ligand regulation, and therapeutic potential. RESULTS: The research indicates that GPR3 is involved in neuronal survival, synaptic plasticity, and microglial activity via the cAMP/PKA, PI3K/Akt, and β - arrestin pathways. It promotes amyloid - β formation in Alzheimer's disease (AD), yet provides neuroprotection in Parkinson's disease (PD) models. It may contribute to anxiety/depression - like states, maintain oocyte meiotic arrest in the ovary, and activate thermogenic genes in adipose tissue. GPR3 modulates immune responses. Using oleic acid (OA) and diphenyleneiodonium (DPI) as activators, and AF64394 and cannabidiol (CBD) as antagonists, it shows potential in disease models. CONCLUSION: GPR3 acts as a central molecular hub integrating neural, metabolic, immune, and reproductive signaling, highlighting its potential as a therapeutic target for chronic multisystem disorders. However, its dual roles in certain pathologies and translation challenges necessitate further research.

Humans↗

PubMed2026

A systematic review of macaque brain stimulation: Trends and future directions.

Neurostimulation techniques can powerfully modulate neural circuit activity and provide causal insights into the relationship between brain function and behavior. Macaque monkeys have long been a key animal model for brain stimulation studies. While stimulating the macaque brain with one or a few electrodes has already taught us much about brain function and dysfunction, recent technological advances promise a future with more precise stimulation using many more electrodes. However, such possibilities also increase the number of choices an experimenter has when designing their study. We can learn from a rich past, but a comprehensive overview of which brain regions have been studied and with what stimulation parameters is lacking. Here, we present a PRISMA-compliant systematic review of 734 macaque brain stimulation studies using electrical and/or optogenetic stimulation. We find a striking bias in which brain areas have traditionally been stimulated: a mere 10 brain regions account for half of all studies, with the remainder of studies investigating approximately 150 other areas. Across studies, stimulation frequency robustly predicted direct behavioral effects independent of brain region, while amplitude did not. Future studies could more systematically explore less studied regions through lower stimulation frequencies (e.g., 20-50 Hz) alongside established ranges (∼200 Hz). Tools such as fMRI or optical imaging can capture neural circuit engagement evoked by these frequencies, even when behavioral effects are absent or remain subtle. Our synthesis offers a guide towards the next steps in high-channel-count, high-precision stimulation approaches.

Animals↗

PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗

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

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

Mitophagy-mediated ferroptosis involved in 2,5-hexanedione-induced neurotoxicity in rats.

n-Hexane, a widespread environmental and industrial pollutant, poses serious health risks, particularly neurotoxicity. Chronic exposure primarily induces sensorimotor neuropathy via its metabolite 2,5-hexanedione (HD), yet the mechanisms underlying HD-induced neuronal injury remain unclear. Recent evidence implicates ferroptosis, an iron-dependent form of regulated cell death, in neurodegenerative processes. In this study, Sprague-Dawley (SD) rats were exposed to HD to establish a neuropathy model. Ferroptosis involvement was assessed using the iron chelator deferoxamine (DFO) and the ferroptosis inhibitor Ferrostatin-1. The potential role of mitophagy in HD-induced ferroptosis was evaluated by monitoring mitophagy markers and by autophagy inhibition with chloroquine (CQ). In vitro, SH-SY5Y cells were transfected with PINK-1 siRNA to explore mitophagy-mediated regulation of ferroptosis. HD exposure led to iron accumulation, lipid peroxidation, mitochondrial abnormalities, and decreased GPX4 in rat spinal neurons. DFO or ferrostatin-1 treatment ameliorated these changes and preserved mitochondrial integrity. Mechanistic analyses revealed HD-induced activation of mitophagy, as shown by upregulation of Beclin-1, LC3II, Drp-1, and PINK-1, with concomitant downregulation of P62 in spinal mitochondria. CQ suppressed mitophagy, reduced iron deposition and lipid peroxidation, and improved motor function. Similarly, PINK-1 knockdown in SH-SY5Y cells mitigated HD-induced mitophagy and ferroptosis. These findings demonstrate that HD induces neuronal ferroptosis via mitophagy activation. Inhibition of ferroptosis or mitophagy effectively attenuates HD-induced neurotoxicity, suggesting potential therapeutic strategies to reduce neural damage from environmental n-hexane exposure.

Animals↗

PubMed2026

Subacute and long-term changes in cognitive functioning after administration of classic psychedelics, MDMA and ketamine: A systematic review of clinical and preclinical evidence.

Psychedelic agents induce a window of heightened neuroplasticity that extends beyond acute intoxication, during which neural circuits are more amenable to change. This period may facilitate changes in cognition relevant to the treatment of psychiatric disorders. This systematic review synthesised clinical and preclinical evidence of subacute and long-term (≥1 day) effects of classic and non-classic psychedelics on cognition. MEDLINE, EMBASE, APA PsycInfo and Web of Science were searched to identify human and animal studies investigating psychedelics and cognition (executive function, attention, decision-making). Sixty-seven (47 clinical, 20 preclinical) articles met inclusion criteria. Psilocybin demonstrated the most consistent evidence of subacute and longer-term cognitive improvement, particularly in cognitive flexibility and attention. Ketamine showed enhancement across cognitive domains, although findings were heterogeneous. LSD and DMT showed no consistent subacute changes, while MDMA was associated with transient cognitive impairments that resolved within days. Risk of bias assessments revealed selective outcome reporting, poor reporting of missing data and inadequate methodological detail, limiting the confidence of findings. Current evidence provides preliminary support for subacute changes in cognition following administration of select psychedelic agents. Cognitive improvements were more frequently seen in psychiatric populations than healthy subjects, which may reflect a remediation of existing cognitive deficit rather than enhancement above normal functioning. Adequately powered, controlled studies with standardised reporting of cognitive outcomes are required to determine the magnitude, durability and clinical relevance of psychedelic-associated cognitive change.

Hallucinogens↗

PubMed2026

Identification and characterization of G protein-coupled receptors in the nocturnal halictid bee Megalopta genalis.

G protein-coupled receptors (GPCRs) are one of the largest families of membrane proteins in insects, regulating vision, neural signal transduction, and various physiological behaviors. Megalopta genalis exhibits a unique facultatively eusocial lifestyle and possesses adaptations for nocturnal activity; however, its GPCR family has not yet been systematically characterized. In this study, we performed genome-wide identification, phylogenetic analysis, and expression profiling of GPCRs in M. genalis by integrating genomic annotation and transcriptomic analysis. The results showed that a total of 99 GPCRs were identified in the genome of M. genalis, which were classified into four major families. Here, we show that M. genalis has undergone lineage-specific GPCR repertoire remodeling, marked by the expansion of novel orphan receptors and the systematic loss of multiple receptor subtypes, such as the neuropeptide receptors MIP-R and NPFR. Moreover, opsins have formed a diverse array of combinations and non-GPCR odorant receptors have undergone significant expansion via tandem duplication. Together, these features may represent part of the molecular repertoire associated with the adaptation of M. genalis to a nocturnal lifestyle. Furthermore, transcriptomic analysis revealed distinct spatiotemporal expression divergence within each of the Mth/Mthl and Fz GPCR families, suggesting functional specialization across development and adult tissues. This study provides the first systematic identification and initial functional characterization of GPCRs in M. genalis, revealing an evolutionary pattern characterized by the coexistence of contraction and expansion within the GPCR family. These findings lay a foundation for further studies aimed at elucidating the roles of these GPCRs in regulating M. genalis physiology and behavior.

Animals↗

PubMed2026

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics↗

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

Age-related differences in motor unit behaviours and maximal strength: A systematic review and meta-analysis.

Ageing is associated with a decline in strength; however, the neural mechanisms underpinning these changes remain poorly understood. Motor unit discharge rate (MUDR) and recruitment threshold (MURT) regulate the magnitude of motoneuron output through rate coding and orderly recruitment, while discharge rate variability (MUDRV) reflects the steadiness of motoneuron output. Yet, age-related differences in these properties remain inconsistent across the literature. Therefore, this systematic review and meta-analysis quantified age-related differences in motor unit behaviours and their contribution to maximal isometric strength. Electronic databases (Medline, Embase, Scopus, PsycINFO, Ovid Emcare, CENTRAL, and Web of Science) were searched up to May 2025, yielding 1493 records; of these, 48 studies met the inclusion criteria. Standardised mean differences (SMDs) were calculated using random-effects models to compare older and younger adults, and methodological quality was assessed using the AXIS tool. Older adults exhibited markedly lower maximal strength than younger adults (SMD = -1.01; 95% CI -1.22, -0.79). MUDR was lower in older adults across all contraction intensities, with greater reductions at high forces (> 60% maximal voluntary contraction (MVC): SMD = -0.65; 95% CI -0.96, -0.34) compared to low forces (< 30% MVC: SMD = -0.34; 95% CI -0.50, -0.18). Discharge rate variability was greater (SMD = 0.44; 95% CI 0.15, 0.72), whereas recruitment thresholds relative to MVC were lower (SMD = -0.42; 95% CI -0.80, -0.03) in older adults. Collectively, these findings suggest that age-related alterations in motor unit discharge behaviour may contribute, at least in part, to reduced maximal strength in older adults.

Aging↗