Electroencephalography (EEG) and magnetic resonance imaging (MRI), as neuroimaging technologies, provided objective and visualized technical tools for analyzing the brain effect mechanisms of acupuncture and moxibustion from the perspectives of brain structure, function, metabolism, and hemodynamics. The advancement of artificial intelligence (AI) algorithms can compensate for issues such as the large and scattered nature of neuroimaging data, inconsistent quality, and high heterogeneity of image information. The integration of AI with neuroimaging can facilitate individualized, intelligent, and precise prediction of acupuncture and moxibustion effects, enable intelligent classification of differential acupuncture responses, and identify brain activation patterns. This paper focuses on EEG and MRI, analyzing how machine learning and deep learning optimize multimodal neuroimaging data and their applications in the study of acupuncture and moxibustion brain effects mechanisms. Furthermore, it highlights current research gaps and limitations to provide insights for future studies on acupuncture brain effects mechanisms.
Humans ; Acupuncture Therapy ; Brain/physiology* ; Moxibustion ; Neuroimaging/methods* ; Artificial Intelligence ; Magnetic Resonance Imaging ; Electroencephalography

Intelligence encompasses various abilities, including logical reasoning, comprehension, self-awareness, learning, planning, creativity, and problem-solving. Extensive research and practical experience suggest that there are sex differences in intellectual development, with females typically maturing earlier than males. However, the key genes and molecular network mechanisms underlying these sex differences in intellectual development remain unclear. To date, Genome-Wide Association Studies (GWAS) have identified 507 genes that are significantly associated with intelligence. This study first analyzed RNA sequencing data from different stages of brain development (from BrainSpan), revealing that during the late embryonic stage, the average expression levels of intelligence-related genes are higher in males than in females, while the opposite is observed during puberty. This study further constructed interaction networks of intelligence-related genes with sex-differential expression in the brain, including the prenatal male network (HELP-M: intelligence genes with higher expression levels in prenatal males) and the pubertal female network (HELP-F: intelligence genes with higher expression levels in pubertal females). The findings indicate that the key genes in both networks are Ep300 and Ctnnb1. Specifically, Ep300 regulates the transcription of 53 genes in both HELP-M and HELP-F, while Ctnnb1 regulates the transcription of 45 genes. Ctnnb1 plays a more prominent role in HELP-M, while Ep300 is more crucial in HELP-F. Finally, this study conducted sequencing validation on rats at different developmental stages, and the results indicated that in the prefrontal cortex of female rats during adolescence, the expression levels of the intelligence genes in HELP-F, as well as key genes Ep300 and Ctnnb1, were higher than those in male rats. These genes were also involved in neurodevelopment-related biological processes. The findings reveal a sex-differentiated intelligence gene network and its key genes, which exhibit varying expression levels during the neurodevelopmental process.
Female ; Intelligence/physiology* ; Male ; Sex Characteristics ; Animals ; Brain/growth & development* ; E1A-Associated p300 Protein/physiology* ; beta Catenin/physiology* ; Transcriptome ; Rats ; Gene Expression Profiling ; Genome-Wide Association Study

Chemical communication in plant-microbiome and intra-microbiome interactions weaves a complex network, critically shaping ecosystem stability and agricultural productivity. This non-contact interaction is driven by small-molecule signals that orchestrate crosstalk dynamics and beneficial association. Plants leverage these signals to distinguish between pathogens and beneficial microbes, dynamically modulate immune responses, and secrete exudates to recruit a beneficial microbiome, while microbes in turn influence plant nutrient acquisition and stress resilience. Such bidirectional chemical dialogues underpin nutrient cycling, co-evolution, microbiome assembly, and plant resistance. However, knowledge gaps persist regarding validating the key molecules involved in plant-microbe interactions. Interpreting chemical communication requires multi-omics integration to predict key information, genome editing and click chemistry to verify the function of biomolecules, and artificial intelligence (AI) models to improve resolution and accuracy. This review helps advance the understanding of chemical communication and provides theoretical support for agriculture to cope with food insecurity and climate challenges.
Microbiota/physiology* ; Plants/microbiology* ; Artificial Intelligence ; Ecosystem

The metabolic reactions in cells, whether spontaneous or enzyme-catalyzed, form a highly complex metabolic network closely related to cellular physiological metabolic activities. The reconstruction of cellular physiological metabolic network models aids in systematically elucidating the relationship between genotype and growth phenotype, providing important computational biology tools for precisely characterizing cellular physiological metabolic activities and green biomanufacturing. This paper systematically introduces the latest research progress in different types of cellular physiological metabolic network models, including genome-scale metabolic models (GEMs), kinetic models, and enzyme-constrained genome-scale metabolic models (ecGEMs). Additionally, our paper discusses the advancements in the automated construction of GEMs and strategies for condition-specific GEM modeling. Considering artificial intelligence offers new opportunities for the high-precision construction of cellular physiological metabolic network models, our paper summarizes the applications of artificial intelligence in the development of kinetic models and enzyme-constrained models. In summary, the high-quality reconstruction of the aforementioned cellular physiological metabolic network models will provide robust computational support for future research in quantitative synthetic biology and systems biology.
Metabolic Networks and Pathways/physiology* ; Models, Biological ; Artificial Intelligence ; Systems Biology ; Kinetics ; Cell Physiological Phenomena ; Computational Biology ; Synthetic Biology ; Humans

Rapid serial visual presentation (RSVP) is a type of psychological visual stimulation experimental paradigm that requires participants to identify target stimuli presented continuously in a stream of stimuli composed of numbers, letters, words, images, and so on at the same spatial location, allowing them to discern a large amount of information in a short period of time. The RSVP-based brain-computer interface (BCI) can not only be widely used in scenarios such as assistive interaction and information reading, but also has the advantages of stability and high efficiency, which has become one of the common techniques for human-machine intelligence fusion. In recent years, brain-controlled spellers, image recognition and mind games are the most popular fields of RSVP-BCI research. Therefore, aiming to provide reference and new ideas for RSVP-BCI related research, this paper reviewed the paradigm design and system performance optimization of RSVP-BCI in these three fields. It also looks ahead to its potential applications in cutting-edge fields such as entertainment, clinical medicine, and special military operations.
Humans ; Brain-Computer Interfaces ; Electroencephalography/methods* ; Brain/physiology* ; Artificial Intelligence ; Photic Stimulation/methods*

OBJECTIVES: To explore the difference of eye movement characteristics between uncooperative and cooperative subjects with mental disorder after cerebral trauma. METHODS: Thirty-nine subjects which needed psychiatric impairment assessment were selected. According to the binomial forced-choice digit memory test （BFDMT）, all subjects were divided into cooperative and uncooperative groups. The subjects were asked to take the image completion test from Wechsler adult intelligence scale. Meanwhile, the data of eye movement track, fixation, saccade, pupil and blink were recorded by the track system of eye movement. RESULTS: There were significantly differences （P<0.05） in the data of saccade between cooperative （10 cases） and uncooperative groups （29 cases）. The frequency, time, amplitude, acceleration of saccadic in uncooperative group were significantly higher than cooperation group. The saccade latencies of cooperation group increased more than uncooperative group. There was a significant difference （P<0.05） in total discrete distance, average distance and total time of fixation between two groups, while the average duration time, number and frequency of fixation had no significantly difference （P>0.05） between two groups. And the blink frequency of cooperation group was higher than uncooperative group. CONCLUSIONS Eye movement can be an objective index for the primary judgment of cooperation level.
Adult ; Eye Movement Measurements ; Eye Movements/physiology* ; Humans ; Intelligence Tests ; Saccades/physiology* ; Wechsler Scales

BACKGROUNDThe cognitive function of children with either attention deficit hyperactivity disorder (ADHD) or learning disabilities (LDs) is known to be impaired. However, little is known about the cognitive function of children with comorbid ADHD and LD. The present study aimed to explore the cognitive function of children and adolescents with ADHD and learning difficulties in comparison with children with ADHD and healthy controls in different age groups in a large Chinese sample.

METHODSTotally, 1043 participants with ADHD and learning difficulties (the ADHD + learning difficulties group), 870 with pure ADHD (the pure ADHD group), and 496 healthy controls were recruited. To investigate the difference in cognitive impairment using a developmental approach, all participants were divided into three age groups (6-8, 9-11, and 12-14 years old). Measurements were the Chinese-Wechsler Intelligence Scale for Children, the Stroop Color-Word Test, the Trail-Making Test, and the Behavior Rating Inventory of Executive Function-Parents (BRIEF). Multivariate analysis of variance was used.

RESULTSThe results showed that after controlling for the effect of ADHD symptoms, the ADHD + learning difficulties group was still significantly worse than the pure ADHD group, which was, in turn, worse than the control group on full intelligence quotient (98.66 ± 13.87 vs. 105.17 ± 14.36 vs. 112.93 ± 13.87, P < 0.001). The same relationship was also evident for shift function (shifting time of the Trail-Making Test, 122.50 [62.00, 194.25] s vs. 122.00 [73.00, 201.50] s vs. 66.00 [45.00, 108.00] s, P< 0.001) and everyday life executive function (BRIEF total score, 145.71 ± 19.35 vs. 138.96 ± 18.00 vs. 122.71 ± 20.45, P < 0.001) after controlling for the effect of the severity of ADHD symptoms, intelligence quotient, age, and gender. As for the age groups, the differences among groups became nonsignificant in the 12-14 years old group for inhibition (meaning interference of the Stroop Color-Word Test, 18.00 [13.00, 25.00] s vs. 17.00 [15.00, 26.00] s vs. 17.00 [10.50, 20.00] s , P = 0.704) and shift function (shifting time of the Trail-Making Test, 62.00 [43.00, 97.00] s vs. 53.00 [38.00, 81.00] s vs. 101.00 [88.00, 114.00] s, P = 0.778).

CONCLUSIONSChildren and adolescents with ADHD and learning difficulties have more severe cognitive impairment than pure ADHD patients even after controlling for the effect of ADHD symptoms. However, the differences in impairment in inhibition and shift function are no longer significant when these individuals were 12-14 years old.

Adolescent ; Attention Deficit Disorder with Hyperactivity ; physiopathology ; Child ; Cognition ; physiology ; Cross-Sectional Studies ; Executive Function ; physiology ; Female ; Humans ; Intelligence Tests ; Learning Disorders ; physiopathology ; Male

PURPOSE: A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women compared to the ability of conventional clinical decision tools. MATERIALS AND METHODS: We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Examination Surveys. The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests, artificial neural networks (ANN), and logistic regression (LR) based on simple surveys. The machine learning models were compared to four conventional clinical decision tools: osteoporosis self-assessment tool (OST), osteoporosis risk assessment instrument (ORAI), simple calculated osteoporosis risk estimation (SCORE), and osteoporosis index of risk (OSIRIS). RESULTS: SVM had significantly better area under the curve (AUC) of the receiver operating characteristic than ANN, LR, OST, ORAI, SCORE, and OSIRIS for the training set. SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0% at total hip, femoral neck, or lumbar spine for the testing set. The significant factors selected by SVM were age, height, weight, body mass index, duration of menopause, duration of breast feeding, estrogen therapy, hyperlipidemia, hypertension, osteoarthritis, and diabetes mellitus. CONCLUSION: Considering various predictors associated with low bone density, the machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.
Aged ; *Artificial Intelligence ; Bone Density/*physiology ; Female ; Humans ; Middle Aged ; Osteoporosis, Postmenopausal

Heart sound is a physiological parameter with individual characteristics generated by heart beat. To do the individual classification and recognition, in this paper, we present our study of using wavelet transform in the signal denoising, with the Mel-Frequency cepstrum coefficients (MFCC) as the feature parameters, and propose a research of reducing the dimensionality through principal components analysis (PCA). We have done the preliminary study to test the feasibility of biometric identification method using heart sound. The results showed that under the selected experimental conditions, the system could reach a 90% recognition rate. This study can provide a reference for further research.
Algorithms ; Artificial Intelligence ; Heart Sounds ; physiology ; Humans ; Individuality ; Pattern Recognition, Physiological ; physiology ; Principal Component Analysis ; Signal Processing, Computer-Assisted ; Wavelet Analysis

BACKGROUNDCesarean section births have been steadily increasing over the past decade and have become an epidemic in China. Cesarean delivery on maternal request is a major contributor to this upward trend, and there has been of much concern about its impact on maternal and child health. Most of mothers believe that cesarean delivery on maternal request can improve the child's intelligence, but direct evidence is sparse. In this cohort study, we aimed to directly assess the impact of cesarean delivery on maternal request on childhood intelligence.

METHODSIntelligence quotient (IQ) of 4144 preschool children from 21 cities/counties of Zhejiang and Jiangsu province whose mothers were registered in a population-based perinatal surveillance program during 1993-1996 was assessed with Chinese Wechsler Young Children Scale of Intelligence (C-WYCSI) in 2000. The outcomes were full-scale IQ, verbal IQ, and performance IQ of C-WYCSI. Mode of delivery and covariates were obtained from the surveillance program. We estimated unadjusted and adjusted effects of cesarean delivery on maternal request and assisted vaginal delivery on IQ scores compared with spontaneous vaginal delivery using regression analysis.

RESULTSThe mean full-scale, verbal, and performance IQ for all children was 99.3 ± 16.1, 93.6 ± 17.7, and 105.3 ± 14.3. In crude analysis, cesarean delivery on maternal request versus spontaneous vaginal delivery was associated with an increase of 3.9 (95% confidence interval, 0.6 to 7.2) points in full-scale IQ, 4.8 (1.2 to 8.4) points in verbal IQ, and 2.4 (-0.6 to 5.3) points in performance IQ. After adjusting for maternal education, occupation, and IQ, the advantage was reduced to 1.6 (-1.3 to 4.5), 2.3 (-0.8 to 5.5), and 0.6 (-2.0 to 3.3) points for full-scale, verbal, and performance IQ, respectively. Assisted vaginal delivery versus spontaneous vaginal delivery was not associated with IQ scores in any analysis.

CONCLUSIONNeither cesarean delivery on maternal request nor assisted vaginal delivery affected children's IQ.

Cesarean Section ; adverse effects ; Child ; Child, Preschool ; Female ; Humans ; Intelligence ; physiology ; Intelligence Tests ; Male ; Pregnancy