工程技术最新文献

An open-level, single-arm, phase-4 clinical trial was carried out to assess the safety and potential benefits of micronized coated ferric pyrophosphate (MEFP) in patients with iron deficiency anaemia (IDA). For 12 weeks, 60 patients between the ages of 18 and 60 with moderate IDA were randomly received MEFP by PO daily. The efficacy endpoints as haemoglobin levels, mean corpuscular haemoglobin (MCH), mean cell haemoglobin concentration (MCHC), packed cell volume (PCV), red blood cell count (RBC), serum ferritin and transferrin saturation (%) were measured. Adverse event reports and physical examinations were performed as a measure of safety assessment. The results revealed that haemoglobin, MCV, MCHC, serum ferritin, transferrin saturation (%), PCV and RBC increased significantly from baseline. Fewer occurrences were observed in a few patients, and their adverse events were minimal. There was no adverse effect on liver or renal functions. Few minor improvements were noticed at the completion of the study. In conclusion, MEFP appears to be effective in IDA and well tolerated, with a favourable safety profile. MEFP is an effective, safe therapeutic alternative in IDA subjects for increasing haemoglobin concentration and iron stores along with improvement of symptoms related to anaemia.

Parkinson's disease (PD) is a neurodegenerative disorder that affects both motor and cognitive functions. An objective and easily measurable digital marker is crucial for improving the diagnosis and monitoring of PD. Since gait is a complex activity that requires both motor control and cognitive input, this study assumes that kinetic parameters of the foot sensitive to the cognitive load (dual-tasking) for healthy adults can be used to diagnose PD. In this study, walking with a cognitive task has been conducted on healthy subjects, the kinetic parameters have been calculated with algorithms of inverse dynamics in Opensim. Subsequently, the moment-related variables, including the bend and force of the plantar surface, were collected from 13 patients with PD and 32 healthy controls using the wearable system. Statistical analysis of the focused kinetic parameters indicates that the moment of the metatarsophalangeal joint has a significant difference between dual-task walking and single walking. The experimental results demonstrate that features extracted from the bend and force signal of the plantar surface can diagnose PD with an average accuracy of 95.55% with 5-fold cross validation. It demonstrates that kinetic data from the foot captured by wearable sensors can serve as an objective digital marker for PD.

[This corrects the article DOI: 10.1007/s11571-024-10119-8.].

Emotion Recognition generally involves the identification of the present mental state or psychological conditions of the human while interacting with others. Among the various modalities, Electroencephalography is the most deceptive emotion recognition technique because of its ability to characterize brain activities accurately. Several emotion recognition methods have been designed utilizing Deep Learning approaches from EEG signals. Yet, their inability to capture the complex features and the occurrence of the overfitting problems with increased computational complexity affected their extensive application. Therefore, this research proposes the Cross-Connected Distributive Learning-enabled Graph Convolutional Network (C2DGCN) for effective emotion recognition. Specifically, the cross-connected distributive learning in the C2DGCN enables extensive feature sharing and integration, thus reducing the computation complexity and improving the accuracy. Further, the application of the Statistical Time-Frequency Signal descriptor aids in the extraction of complex features and mitigates the overfitting issue. The experimental validation revealed the effectiveness of the C2DGCN by achieving a high accuracy of 97.73%, sensitivity of 98.32%, specificity of 98.22%, and precision of 98.32% with 90% of training using the SEED-IV dataset. For the evaluation using the DEAP dataset, the proposed C2DGCN model reaches an accuracy of 97.66%, precision of 97.98%, sensitivity of 97.25%, and specificity of 98.07%.

Vertigo, a prevalent neurovestibular disorder, arises from dysfunction in the vestibular system and often lacks precise, personalized treatments. This study proposes a bio-inspired spiking neural network (SNN) model that simulates vestibular dysfunction and adaptive recovery using Leaky Integrate-and-Fire (LIF) neurons with spike-timing-dependent plasticity (STDP). The architecture mimics the vestibular pathway through biologically plausible layers: hair cells, afferents, and cerebellar integrators, and models pathological states such as hair cell hypofunction and synaptic disruption. A reinforcement-based feedback mechanism enables the simulation of therapy-induced plasticity, resulting in a 48-62% drop and 38% recovery in cerebellar spike activity during adaptation epochs. The model demonstrates real-time feasibility, with an average simulation runtime of  4 s per epoch on standard hardware. Its design is scalable and well-suited for future deployment on neuromorphic platforms (e.g., Loihi, SpiNNaker). Its modular and interpretable design enables in silico testing of rehabilitation strategies, real-time monitoring of dysfunction, and future personalization using clinical datasets. This work establishes a computational foundation for AI-driven vestibular therapy that is adaptive, explainable, and hardware compatible.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10368-1.

Accurate localization of the seizure onset zone (SOZ) is critical for successful surgery in drug-resistant epilepsy (DRE). To investigate the alterations of network characteristics between the SOZ and non-seizure onset zones (NSOZ) across different seizure stages, the intracranial electroencephalogram (iEEG) data based brain networks from 29 DRE patients have been constructed using the weighted phase lag index (WPLI) and phase transfer entropy (PTE), respectively. Then, graph theory metrics, such as eigenvector centrality, betweenness centrality, in-degree and out-degree, are calculated to compare network characteristics of SOZ and NSOZ nodes across interictal, pre-ictal, early-ictal and post-ictal periods in multiple frequency bands. Statistical analyses demonstrate that the SOZ exhibits significantly higher eigenvector centrality and betweenness centrality in the beta and gamma frequency bands, serving as network hubs and primary sources of information outflow. By contrast, the NSOZ shows elevated centrality only in the theta and alpha frequency bands during non-ictal states. Moreover, during the pre-ictal to early-ictal transition, the SOZ progressively evolves into hub nodes with enhanced outflow and reduced inflow, whereas the NSOZ shifts toward non-hub status with increased inflow. Importantly, the random forest model utilizing out-degree features of early-ictal gamma frequency band can effectively identify the SOZ, and achieve an area under the curve (AUC) of 0.82. Overall, these findings offer a novel network-based perspective on the state-dependent alterations of epileptic seizures in DRE and contribute to the treatment of epilepsy.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10400-4.

Acupuncture modulates cognitive functions through acupoint stimulation and demonstrates significant regulatory effects on brain disorders. However, the underlying neurodynamic mechanisms of acupuncture remain unclear due to a lack of effective measures of brain activity. In this study, we developed an acupuncture-related potential (ARP) method based on Electroencephalogram (EEG) to elucidate the dynamic representation mechanisms underlying acupuncture stimulation. By analyzing ARP signal features and functional networks to capture stimulus-evoked brain activity, we derived spatiotemporal representations of neural manifolds and located across whole brain regions. It is exhibited that acupuncture induced significant four-phase event-related potentials (ERPs) waveforms predominantly in the parietal, frontal, central, and temporal lobes, with the parietal lobe exhibiting the highest amplitude at the P1 component (first positive peak). Latency gradients confirmed that the cortical neural activity originated in the parietal lobe and propagated through the central region to the frontal and temporal lobes. Dynamic network analysis revealed phase-specific reorganization: local frontal propagation (P1 component), global integration (P2 component), and novel topological pattern formation (P3 component). Neural manifold analysis uncovered a low-dimensional, ring-shaped representation encompassing the frontal, parietal, central, and temporal lobes. Acupuncture modulates brain function by activating key parietal lobe nodes, triggering distance-attenuated inter-regional signal transmission that dynamically reorganizes functional networks for multi-regional collaboration. The neural manifold representation revealed perception and integration of mechanisms of acupuncture information in the human brain. This ARP method provided a novel framework for investigating acupuncture-modulated spatiotemporal brain dynamics while enabling quantitative evaluation of its therapeutic effects.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10408-w.

Modern AI systems excel at pattern recognition and task execution, but they often fall short of replicating the layered, self-referential structure of human thought that unfolds over time. In this paper, we present a mathematically grounded and conceptually simple framework based on smoothed step functions-sigmoid approximations of Heaviside functions-to model the recursive development of mental activity. Each cognitive layer becomes active at a specific temporal threshold, with the abruptness or gradualness of activation governed by an impressiveness parameter [Formula: see text], which we interpret as a measure of emotional salience or situational impact. Small values of [Formula: see text] represent intense or traumatic experiences, producing sharp and impulsive responses, while large values correspond to persistent background stress, yielding slow but sustained cognitive activation. We formulate the recursive dynamics of these cognitive layers and demonstrate how they give rise to layered cognition, time-based attention, and adaptive memory reinforcement. Unlike conventional memory models, our approach captures thoughts and recall events through a recursive, impressiveness-sensitive pathway, leading to context-dependent memory traces. This recursive structure offers a new perspective on how awareness and memory evolve over time, and provides a promising foundation for designing artificial systems capable of simulating recursive, temporally grounded consciousness.

In biological neurons, synapses receive external stimuli to induce firing patterns. While the rapid generation of synapses regulates neural activity. In this paper, we use a magnetic-flux controlled memristor (MFCM) as a synapse to connect two functional neurons, establish the new coupled neurons, and study the synchronization characteristics. Firstly, we connect two neurons using memristive synapses, and derive the equations of the coupled neurons based on Kirchhoff's voltage law. Furthermore, we calculate the energy of the memristive coupling channels, and obtain the energy difference between the coupled neurons. Secondly, we propose a criterion for exponential growth controlled by energy difference. By setting higher coupling channel strength to establish synaptic connections, energy pumping can be effectively activated. Finally, for three modes, we analyze the energy evolution under the variations of memristive synapses, and find that the coupling channels are adaptively controlled by energy difference. The results show that when the coupling strength through synapses is enhanced, identical neurons can achieve complete synchronization, and different neurons can achieve phase locking. This study clarifies the underlying mechanisms of regulating coupled neurons via memristive synapses and explores how neurons achieve potential energy balance from the perspective of physical fields.

Coriandrum sativum L. (coriander) is a medicinal herb with diverse pharmacological properties, but its molecular mechanism in clear cell renal cell carcinoma (ccRCC) remains unclear. This study aimed to systematically investigate the underlying mechanisms of coriander in ccRCC by multi-omics analysis. Active compounds were screened using Traditional Chinese Medicine Systems Pharmacology (TCMSP) and predicted targets identified via SwissTargetPrediction (STP) and Similarity ensemble approach (SEA). Transcriptomic data from GSE53757 were analysed with WGCNA and intersected with coriander targets. Key genes were selected using LASSO, SVM, and random forest models. NEK6 was further analysed for clinical relevance, methylation, immune association, single-cell expression, molecular docking and molecular dynamics simulation. Fourteen coriander compounds were identified, yielding 22 potential ccRCC-related targets. NEK6 and PYGL were consistently selected by all machine learning algorithms. NEK6 was overexpressed in ccRCC and associated with better prognosis, promoter hypomethylation, and lower mutation rates. NEK6 expression correlated with immune infiltration, particularly macrophages, and was enriched in tumour and myeloid cells at the single-cell level. Molecular docking and molecular dynamics simulation revealed strong and stable binding of luteolin, quercetin, and chryseriol to NEK6. NEK6 may function as a prognostic and immune-regulatory biomarker in ccRCC. Coriander flavonoids could target NEK6 to modulate the immune microenvironment, providing new insight into plant-based therapeutic strategies for ccRCC.