工程技术最新文献

{"title":"AI-guided data-driven kinetic modelling carbon quantum dot-enabled pH-responsive CMC/CeO₂ nanocarriers for quercetin delivery and <i>in vitro</i> evaluation in lung cancer cells.","authors":"Nikoo Baghal Darbandi, Zeinab Rostami Dehka, Mehrab Pourmadadi, Salar Mohammadi Shabestari, Elham Hatami Monfared","doi":"10.1080/21691401.2026.2702868","DOIUrl":"https://doi.org/10.1080/21691401.2026.2702868","url":null,"abstract":"<p><p>This work reports the development and systematic evaluation of a carboxymethyl cellulose (CMC)-based nanocarrier system co-loaded with cerium oxide (CeO₂) and carbon quantum dots (CQDs) for pH-responsive delivery of quercetin (QC) and <i>in vitro</i> evaluation in lung cancer cells. The nanocarriers were prepared using a water-in-oil-in-water (W/O/W) double emulsion approach, yielding spherical particles with an average size of approximately 134 nm and a high positive surface charge (+66 mV), indicative of favourable colloidal stability. FESEM analysis confirmed a uniform morphology and compact internal structure. The incorporation of CeO₂ appears to reinforce the polymer matrix, contributing to improved drug encapsulation. The optimized formulation exhibited high encapsulation efficiency (88%) and drug loading capacity (47%), outperforming CeO₂-free systems. <i>In vitro</i> release studies demonstrated a clear pH-dependent biphasic behaviour, with significantly faster release under pH 5.4 compared to physiological pH (7.4), reaching 98% and 58% after 96 h, respectively. Drug release followed the Higuchi model, suggesting diffusion-controlled kinetics, while the Korsmeyer-Peppas model indicated a non-Fickian mechanism. An AI-guided nonlinear modelling workflow was used to extract interpretable kinetic descriptors directly from experimental release data. Biological evaluation revealed enhanced anticancer activity against A549 cells, with viability reduced to 49.1%, while maintaining high biocompatibility towards L929 cells.</p>","PeriodicalId":8736,"journal":{"name":"Artificial Cells, Nanomedicine, and Biotechnology","volume":"54 1","pages":"337-357"},"PeriodicalIF":5.6,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148468579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification and multi-layered experimental validation of feature genes related to butyrate metabolism and oxidative stress in sepsis.","authors":"Qinglu Fan, Renwei Luo, Lingxia Qin, Zhihao Nie, Qingyun Chen, Shujian Wei, Lingxuan Dan, Guang Li, Genhua Mu, Songping Xie","doi":"10.1080/21691401.2026.2658990","DOIUrl":"10.1080/21691401.2026.2658990","url":null,"abstract":"<p><p>The mechanisms underlying sepsis remain poorly understood and specific therapeutic options remain lacking. Accumulating evidence suggests that butyrate metabolism and oxidative stress (OS) are involved in the pathogenesis of sepsis. This study aims to elucidate molecular characteristics of butyrate metabolism and OS-related genes in sepsis and find potential therapeutic targets. Using sepsis-related datasets, 876 differential expression genes (DEGs) were screened and intersected with butyrate metabolism and OS-related genes to yield 8 differentially expressed butyrate metabolism and OS-related genes (DEBORGs). Machine learning algorithms finally identified 3 feature genes (ALDH1A1, CYP1B1, and GADD45A) with favourable diagnostic values for sepsis. CIBERSORT immune infiltration analysis demonstrated strong associations between feature genes and the infiltration proportions of various immune cell types, such as monocyte, macrophage, and neutrophil. Cellular experiment and prospectively collected clinical samples were utilized to validate the expression levels and diagnostic efficacy of feature genes. The scRNA-seq analysis indicated that monocyte plays a crucial role in mediating the effect of feature genes on sepsis. This study elucidates that ALDH1A1, CYP1B1, and GADD45A are feature genes linked to butyrate metabolism and OS in sepsis, with monocytes being the primary cells involved, providing novel insights into the sepsis pathogenesis and potential therapeutic targets.</p>","PeriodicalId":8736,"journal":{"name":"Artificial Cells, Nanomedicine, and Biotechnology","volume":"54 1","pages":"332-345"},"PeriodicalIF":5.6,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147760480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The write-cost bottleneck: energy constraints on continual learning in brains and machines.","authors":"Bingru Xu, Hao Shen, Yuguo Yu","doi":"10.1007/s11571-026-10508-1","DOIUrl":"10.1007/s11571-026-10508-1","url":null,"abstract":"&lt;p&gt;&lt;p&gt;In recent decades, neural energy-budget research has extensively quantified signaling costs-the \"read\" side of computation-including action potentials, synaptic transmission, and ionic homeostasis. Yet the metabolic cost of learning itself remains far less studied. The \"write\" side-encompassing synaptic plasticity (LTP, LTD, and homeostatic scaling), receptor trafficking, protein synthesis, spine remodeling, and long-term consolidation-is still poorly understood. We argue that these write costs, although smaller than signaling costs in their time-averaged share of the mature brain's energy budget, constitute a real peak-throughput bottleneck for any intelligent system that must learn continuously within fixed energy constraints. A durable synaptic update aggregates an order-of-magnitude estimate of ~ 10⁴-10⁶ ATP per synapse across multiple coincident processes (receptor trafficking, protein turnover, local translation, actin remodeling, Ca²⁺ handling, and concurrent ionic recovery), with the range reflecting both natural variability and a 1-2 order-of-magnitude uncertainty across independent estimates (Karbowski, 2019). This per-synapse multi-process sum is larger than the protein-synthesis component alone-a term that standard energy budgets typically fold into housekeeping rather than treat as a distinct plasticity cost. Dense simultaneous writes are therefore unlikely to be sustainable at scale, because active cortical and hippocampal tissue can operate close to its oxidative ceiling during high-demand states, suggesting that many mature cortical circuits operate with limited additional metabolic headroom during high-demand states, plausibly on the order of tens of percent rather than several-fold (Hyder et al., 2013; Yu et al., 2023; Watts et al., 2018). The brain manages this constraint across multiple regimes: awake activity is characterized by sparse population coding and asynchronous irregular firing that preserves metabolic headroom; durable plasticity also occurs during waking but is sparse, neuromodulator- and attention-gated, and supplemented by brief awake replay events; and sleep provides additional write windows-slow-wave sleep offers a low-cost offline window, while REM sleep provides a selective plasticity window in which restricted circuits sustain patterned activity supporting durable plasticity. We recast learning as the selective allocation of scarce writes-metabolic investments that reorganize circuit structure and can reduce future signaling costs at the population level. Gated plasticity, synaptic caching, and sleep-dependent consolidation are, under this view, write-management strategies. At the systems level, selective attention allows the brain to concentrate plasticity on the circuits engaged in a current task while other regions idle, further constraining writes in space as well as time. By contrast, backpropagation in modern machine learning tightly couples reads and writes, which increases update traffic, ampli","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"139"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13373113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148469377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic bi-domain discriminator adversarial network for EEG emotion recognition.","authors":"Leyang Yu, Qingshan She, Zizhuo Wu, Zhaowen Xiao, Ming Meng, Kun Luo","doi":"10.1007/s11571-026-10497-1","DOIUrl":"10.1007/s11571-026-10497-1","url":null,"abstract":"<p><p>Emotions are constantly generated in daily activities. They not only control people's behavioral patterns and thinking decisions, but also affect physical and mental health. Therefore, the emotion recognition technology based on electroencephalogram (EEG) signals has broad application prospects in multiple fields such as human-computer interaction, medical health, and intelligent driving. To address the issues of insufficient labeled data in EEG and significant differences in EEG data among different subjects and at different time periods, this paper introduces the domain adaptation (DA) technique to solve the task of cross-domain EEG emotion recognition under unsupervised conditions. Aiming at the problem that the existing domain adaptation methods ignore the different weights of the global domain and subdomains when reducing domain differences, this paper proposes a dynamic bi-domain discriminator adversarial network (DBDAN). A feature extractor is constructed to extract the low-level domain invariant features of the EEG signals, and a multi-branch domain-specific feature extractor is used to generate domain-specific features. In order to reduce domain differences, adversarial learning for the global domain and subdomains is achieved through a dual-domain discriminator. Meanwhile, dynamic factors are introduced to dynamically adjust adversarial learning, enabling the model to strike a balance between coarse-grained adversarial and fine-grained adversarial and achieve domain adaptation more precisely. The cross-subject accuracy rates on SEED, SEED-IV and DEAP were 89.43%, 75.09% and 63.42%, respectively. These results indicate that our method achieves competitive performance among the evaluated EEG transfer-learning baselines and suggest that dynamic global-subdomain alignment is beneficial for cross-domain EEG emotion recognition.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"126"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13332908/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148390075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEG brain network analysis of mind-wandering characteristics and its modulation by shift work in high-risk industries.","authors":"Qing Xin, Su Hao, Cai Hongbin, Wang Xiaoqin, Wang Jian, Liu Lu, Shen Liaoyuan","doi":"10.1007/s11571-026-10488-2","DOIUrl":"10.1007/s11571-026-10488-2","url":null,"abstract":"<p><p>Mind-wandering (MW), a form of attentional disengagement during operational tasks, has emerged as a critical cognitive risk factor for unsafe behavior in high-risk industries. Moreover, the disruption of circadian rhythms induced by shift work further amplifies the risks associated with MW in operational contexts. However, existing research has largely relied on short duration experiments conducted under controlled laboratory conditions, leaving the neural mechanisms of MW in authentic high risk operational contexts insufficiently understood. Even fewer studies have empirically examined how shift work influences MW from the perspective of brain network organization. In response to this research gap, the present study employs Lab-in-the-Field experiments to analyze the brain network characteristics of workers in high-risk industries, aiming to elucidate the potential mechanisms by which MW and shift work jointly impact operational safety. The results demonstrated that MW significantly prolonged response times, and that shift work further exacerbated this negative effect. EEG based brain network analysis further showed that MW was accompanied by changes in brain network resource allocation, with a particularly evident decline in hub efficiency in the left prefrontal cortex. Moreover, shift work was found to affect cognitive states predominantly by altering the structural topology of brain networks, rather than by simply modulating the overall strength of functional connectivity. This study validates the brain network reorganization underlying MW and provides scientific evidence for developing cognitive state monitoring tools and targeted intervention strategies based on brain network topological features.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"131"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13346414/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148419465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retraction Statement: Down-regulation of long non-coding RNA ITGB2-AS1 inhibits osteosarcoma proliferation and metastasis by repressing Wnt/β-catenin signalling and predicts favourable prognosis.","authors":"","doi":"10.1080/21691401.2026.2708515","DOIUrl":"https://doi.org/10.1080/21691401.2026.2708515","url":null,"abstract":"","PeriodicalId":8736,"journal":{"name":"Artificial Cells, Nanomedicine, and Biotechnology","volume":"54 1","pages":"394"},"PeriodicalIF":5.6,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148598115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retraction Statement: Long non-coding RNA NEAT1 promotes steatosis via enhancement of estrogen receptor alpha-mediated AQP7 expression in HepG2 cells.","authors":"","doi":"10.1080/21691401.2026.2708500","DOIUrl":"https://doi.org/10.1080/21691401.2026.2708500","url":null,"abstract":"","PeriodicalId":8736,"journal":{"name":"Artificial Cells, Nanomedicine, and Biotechnology","volume":"54 1","pages":"392"},"PeriodicalIF":5.6,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148598135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiomics integrative bioinformatics analysis of gene expression characteristics and molecular mechanisms in preeclampsia placental tissue.","authors":"Ni Li, Lin Tian, Haiyun Wei","doi":"10.1080/21691401.2026.2697688","DOIUrl":"https://doi.org/10.1080/21691401.2026.2697688","url":null,"abstract":"<p><p>Preeclampsia (PE) is a severe pregnancy-specific complication characterized by new-onset hypertension and proteinuria after 20 weeks of gestation, which can cause multi-organ damage and life-threatening outcomes for both mothers and foetuses. Its pathogenesis remains incompletely elucidated, with placental dysfunction widely recognized as a core pathogenic factor. This study integrated multiple placental transcriptome and single-cell sequencing datasets from the Gene Expression Omnibus (GEO) database, employing a multi-dimensional bioinformatics approach - including differential expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), machine learning, molecular subtype clustering, single-cell resolution analysis, and intercellular communication analysis - to systematically identify PE-related key genes, construct a diagnostic model, define molecular subtypes, and explore potential molecular mechanisms. Results showed 10 differentially expressed genes (DEGs) were identified in PE placental tissues; WGCNA pinpointed the turquoise module as the core PE-associated module. Further screening using 11 machine learning algorithms identified 9 feature genes with high diagnostic value (DDR1, DIO2, FSTL3, HK2, HTRA4, LEP, SERPINA3, TMEM45A, TREM1). A diagnostic model built with the 'Stepglm[forward]' algorithm exhibited excellent performance in both training and validation sets (average AUC = 0.865). Molecular subtype analysis classified PE samples into two subtypes (C1, C2) with significantly distinct immune infiltration profiles, where the C1 subtype showed higher immune cell infiltration. Single-cell analysis identified 11 cell types in PE placental tissue and highlighted TMEM45A as a key DEG. Intercellular communication analysis revealed the VEGF signalling pathway as the core driver of abnormal cellular crosstalk in PE, primarily mediating signal transduction between villous cytotrophoblast cells (VCT), extravillous trophoblast cells (EVT), and endothelial cells. Hypoxia scoring analysis demonstrated significantly higher hypoxia levels in the PE group compared to normal controls, with TMEM45A expression positively correlated with hypoxia scores. This study provides novel insights into the molecular pathogenesis of PE and offers potential biomarkers and a theoretical basis for its early diagnosis and targeted therapy.</p>","PeriodicalId":8736,"journal":{"name":"Artificial Cells, Nanomedicine, and Biotechnology","volume":"54 1","pages":"358-373"},"PeriodicalIF":5.6,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148519784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Eeg emotion recognition based on channel spatio-temporal multi-dimensional feature extraction network.","authors":"Jingjie Yan, Siya Zhao, Jing Li, Jinsheng Wei, Xiaoli Wang","doi":"10.1007/s11571-026-10509-0","DOIUrl":"10.1007/s11571-026-10509-0","url":null,"abstract":"<p><p>Electroencephalogram (EEG) signals capture the temporal and spatial characteristics of neuronal activities recorded by multiple electrodes and their data complexity is high. In order to effectively extract deeper emotional features from EEG signals, this paper proposes a Channel Spatio-temporal Multi-dimensional Feature Extraction Network (CSMFEN) for EEG emotion recognition. CSMFEN conducts feature learning on EEG signals from three dimensions which include channel domain, spatial domain and temporal domain. In the channel domain, CSMFEN models the relative importance of different EEG electrodes by assigning adaptive weights to each channel, thereby enhancing discriminative channel representations for emotion classification. In the spatial domain, an enhanced dynamic convolution mechanism is proposed to capture inter-channel spatial relationships, where the input signal space size information and convolution kernel weight information are jointly considered. Different attention mechanisms are employed to dynamically generate convolution kernels, enabling flexible spatial feature extraction. In the temporal domain, a Long Short-Term Memory (LSTM) network is utilized to learn the temporal dependencies and sequential dynamics of EEG signals. Experimental validation of CSMFEN is conducted on the SEED and DEAP datasets. The results demonstrate that CSMFEN can effectively learn discriminative representations and achieve competitive emotion recognition performance on both datasets.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"146"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13402277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148599636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph enhanced ContextFusion-EmoNet (CFEN): integrating facial, postural, and environmental cues for emotion recognition in dynamic video scenes.","authors":"Navneet Gupta, R Vishnu Priya, Chandan Kumar Verma","doi":"10.1007/s11571-026-10514-3","DOIUrl":"10.1007/s11571-026-10514-3","url":null,"abstract":"<p><p>Emotion recognition in videos is still difficult due to the fact that affective cues are spread throughout facial expressions, body movements and scene context. Face only approaches tend to perform poorly in most cases because of being affected by such factors as varying illumination, occlusion, camera motion, dynamic backgrounds, and multiple interacting subjects. To overcome these drawbacks, a novel and flexible model called ContextFusion-EmoNet (CFEN) is proposed in this paper to utilize appearance, motion and relational information for robust emotion recognition from video. CFEN uses an Average Contextual Loss (ACL) guided key-frame selection method, incorporating the differences of the VGG16 features and Farneback optical flow weight to select 4 key frames that are rich in motion and extremely informative per video. The selected images are then fed into ResNet18 feature extractor, a temporal context modeler, which is a Transformer encoder, and a deterministic Graph Convolutional Network (GCN) module for modeling the relation between frame tokens. Good intra-dataset performance is shown through extensive experimentation on the four datasets, CAER, CK + , DFEW, and FERV39k, with accuracy rates of 97.71%, 98.00%, 91.43% and 89.71% respectively.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"154"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13451464/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148697314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}