Changes in electroencephalographic power and bicoherence spectra according to depth of dexmedetomidine sedation in patients undergoing spinal anesthesia.

Background: Assessment the depth of dexmedetomidine sedation using electroencephalographic (EEG) features can improve the quality of procedural sedation. Previous volunteer studies of dexmedetomidine-induced EEG changes need to be validated, and changes in bicoherence spectra during dexmedetomidine sedation has not been revealed yet. We aimed to investigate the dexmedetomidine-induced EEG change using power spectral and bicoherence analyses in the clinical setting. Patients and Methods: Thirty-six patients undergoing orthopedic surgery under spinal anesthesia were enrolled in this study. Dexmedetomidine sedation was conducted by the stepwise increase in target effect site concentration (Ce) while assessing sedation levels. Bispectral index (BIS) and frontal electroencephalography were recorded continuously, and the performance of BIS and changes in power and bicoherence spectra were analyzed with the data from the F3 electrode. Results: The prediction probability values for detecting different sedation levels were 0.847, 0.841, and 0.844 in BIS, 95% spectral edge frequency, and dexmedetomidine Ce, respectively. As the depth of sedation increased, δ power increased, but high ß and γ power decreased significantly (P <0.001). α and spindle power increased significantly under light and moderate sedation (P <0.001 in light vs baseline and deep sedation; P = 0.002 and P <0.001 in moderate sedation vs baseline and deep sedation, respectively). The bicoherence peaks of the δ and α-spindle regions along the diagonal line of the bicoherence matrix emerged during moderate and deep sedation. Peak bicoherence in the δ area showed sedation-dependent increases (29.93%±7.38%, 36.72%±9.70%, 44.88%±12.90%; light, moderate, and deep sedation; P = 0.008 and P <0.001 in light sedation vs moderate and deep sedation, respectively; P = 0.007 in moderate sedation vs deep sedation), whereas peak bicoherence in the α-spindle area did not change (22.92%±4.90%, 24.72%±4.96%, and 26.96%±8.42%, respectively; P=0.053). Conclusions: The increase of δ power and the decrease of high-frequency power were associated with the gradual deepening of dexmedetomidine sedation. The δ bicoherence peak increased with increasing sedation level and can serve as an indicator reflecting dexmedetomidine sedation levels.

Simultaneous EEG Acquisition System for Multiple Users: Development and Related Issues.

Social interaction is one of humans' most important activities and many efforts have been made to understand the phenomenon. Recently, some investigators have attempted to apply advanced brain signal acquisition systems that allow dynamic brain activities to be measured simultaneously during social interactions. Most studies to date have investigated dyadic interactions, although multilateral interactions are more common in reality. However, it is believed that most studies have focused on such interactions because of methodological limitations, in that it is very difficult to design a well-controlled experiment for multiple users at a reasonable cost. Accordingly, there are few simultaneous acquisition systems for multiple users. In this study, we propose a design framework for an acquisition system that measures EEG data simultaneously in an environment with 10 or more people. Our proposed framework allowed us to acquire EEG data at up to 1 kHz frequency from up to 20 people simultaneously. Details of our acquisition system are described from hardware and software perspectives. In addition, various related issues that arose in the system's development-such as synchronization techniques, system loads, electrodes, and applications-are discussed. In addition, simultaneous visual ERP experiments were conducted with a group of nine people to validate the EEG acquisition framework proposed. We found that our framework worked reasonably well with respect to less than 4 ms delay and average loss rates of 1%. It is expected that this system can be used in various hyperscanning studies, such as those on crowd psychology, large-scale human interactions, and collaborative brain-computer interface, among others.

The Analysis of Electroencephalography Changes Before and After a Single Neurofeedback Alpha/Theta Training Session in University Students.

The underlying mechanisms of alpha/theta neurofeedback training have not been fully determined. Therefore, this study aimed to test the changes in the brain state feedback during the alpha/theta training. Twenty-seven healthy participants were trained during a single session of the alpha/theta protocol, and the resting quantitative electroencephalography (QEEG) was assessed before and after training. QEEG was recorded at eight scalp locations (F3, F4, C3, C4, T3, T4, O1, and O2), and the absolute power, relative power, ratio of sensory-motor rhythm beta (SMR) to theta (RST), ratio of SMR-mid beta to theta (RSMT), ratio of mid beta to theta (RMT), ratio of alpha to high beta (RAHB), and scaling exponent of detrended fluctuation analysis by each band were measured. The results indicated a significant increase of absolute alpha power, especially the slow alpha band, at all electrodes except T3 and T4. Moreover, the relative alpha power, especially the slow alpha band, showed a significant increase at all electrodes. The relative theta power showed a significant decrease at all electrodes, except T3. A significant decrease in relative beta power, relative lower beta power and relative mid beta power was observed at O1. RST (at C4, O1, and O2), RSMT and RMT (at F4, C4, O1 and O2), and RAHB (at all electrodes) showed significant increase. Scaling exponents at all electrodes except T3 showed a significant decrease. These findings indicate that a one-time session of alpha/theta training might have the possibility to enhance both vigilance and concentration, thus stabilizing the overall brain function.

Effect of Volatile Organic Chemicals in Chrysanthemum indicum Linné on Blood Pressure and Electroencephalogram.

This study identified the volatile organic compounds in the essential oils that are extracted from Chrysanthemum indicum Linné (C. indicum Linné) and investigated the effects of the inhalation of these compounds. We detected a total of 41 volatile organic compounds, including 32 hydrocarbons, four acids, three alcohols, two ketones, and one aldehyde. In a sniffing test, seven types of volatile organic compounds were identified. Furthermore, the volatile organic compounds in C. indicum Linné that were identified were found to be derived from 1,8-cineole and camphor. After inhalation of the essential oils, the subjects' systolic blood pressure and heart rate decreased. This indicates that inhalation of the essential oils extracted from C. indicum Linné provides mental and physical relaxation. We examined the changes in electroencephalogram findings that are observed after C. indicum Linné essential oil inhalation. An increase in theta and alpha waves, which usually appear during relaxation, as well as a decrease in beta and gamma waves, which appear during brain activity such as excessive attention, were noted. These results indicate that C. indicum Linné essential oil inhalation helps to reduce blood pressure and may provide mental and physical relaxation.

Coupling effects on turning points of infectious diseases epidemics in scale-free networks.

BACKGROUND: Pandemic is a typical spreading phenomenon that can be observed in the human society and is dependent on the structure of the social network. The Susceptible-Infective-Recovered (SIR) model describes spreading phenomena using two spreading factors; contagiousness (ß) and recovery rate (γ). Some network models are trying to reflect the social network, but the real structure is difficult to uncover. METHODS: We have developed a spreading phenomenon simulator that can input the epidemic parameters and network parameters and performed the experiment of disease propagation. The simulation result was analyzed to construct a new marker VRTP distribution. We also induced the VRTP formula for three of the network mathematical models. RESULTS: We suggest new marker VRTP (value of recovered on turning point) to describe the coupling between the SIR spreading and the Scale-free (SF) network and observe the aspects of the coupling effects with the various of spreading and network parameters. We also derive the analytic formulation of VRTP in the fully mixed model, the configuration model, and the degree-based model respectively in the mathematical function form for the insights on the relationship between experimental simulation and theoretical consideration. CONCLUSIONS: We discover the coupling effect between SIR spreading and SF network through devising novel marker VRTP which reflects the shifting effect and relates to entropy.

Response surface modelling of the pharmacodynamic interaction between propofol and remifentanil in patients undergoing anaesthesia.

This study describes the pharmacodynamic interaction between propofol and remifentanil. Sixty patients who were scheduled for elective surgery under general anaesthesia (30 males/30 females) were enrolled. Patients were randomly allocated to receive one of 15 combinations of drug levels. Baseline electroencephalograms (EEGs) were recorded for 5 minutes prior to administering the drugs. Patients received a target-controlled infusion at one of four predefined doses of propofol (high, 3 µg/mL; medium, 1.5 µg/mL; low, 0.5 µg/mL; or no drug) and of remifentanil (high, 6 or 8 ng/mL; medium, 4 ng/mL; low, 2 ng/mL; or no drug). The occurrence of muscle rigidity, apnoea, and loss of consciousness (LOC) was monitored, and EEGs were recorded during the drug administration phase. Electroencephalographic approximate entropy (ApEn) and temporal linear mode complexity (TLMC) parameters at baseline and under steady state conditions were calculated off-line. Response surfaces were developed to map the interaction between propofol and remifentanil to the probability of occurrence for quantal responses (muscle rigidity, apnoea, LOC) and ApEn and TLMC measurements. Model parameters were estimated using non-linear mixed effects modelling. The response surface revealed infra-additive and synergistic effects for muscle rigidity and apnoea, respectively. The effects of the combined drugs on LOC and EEG parameters (eg, ApEn and TLMC) were additive. The C50 estimates of remifentanil (ng/mL) and propofol (µg/mL) were 9.11 and 130 000 for muscle rigidity, 8.99 and 6.26 for apnoea, 13.9 and 3.04 for LOC, 23.4 and 10.4 for ApEn, and 14.8 and 6.51 for TLMC, respectively. The probability of occurrence for muscle rigidity declined when propofol was combined with remifentanil.

Directed C-H Activation and Tandem Cross-Coupling Reactions Using Palladium Nanocatalysts with Controlled Oxidation.

Controlled oxidation of palladium nanoparticles provided high-valent PdIV oxo-clusters which efficiently promote directed C-H halogenation reactions. In addition, palladium nanoparticles can undergo changes in oxidation states to provide both high-valent PdIV and low-valent Pd0 species within one system, and thus a tandem reaction of C-H halogenation and cross-coupling (C-N, C-C, and C-S bond formation) was successfully established.

Inferring new drug indications using the complementarity between clinical disease signatures and drug effects.

BACKGROUND: Drug repositioning is the process of finding new indications for existing drugs. Its importance has been dramatically increasing recently due to the enormous increase in new drug discovery cost. However, most of the previous molecular-centered drug repositioning work is not able to reflect the end-point physiological activities of drugs because of the inherent complexity of human physiological systems. METHODS: Here, we suggest a novel computational framework to make inferences for alternative indications of marketed drugs by using electronic clinical information which reflects the end-point physiological results of drug's effects on the biological activities of humans. In this work, we use the concept of complementarity between clinical disease signatures and clinical drug effects. With this framework, we establish disease-related clinical variable vectors (clinical disease signature vectors) and drug-related clinical variable vectors (clinical drug effect vectors) by applying two methodologies (i.e., statistical analysis and literature mining). Finally, we assign a repositioning possibility score to each disease-drug pair by the calculation of complementarity (anti-correlation) and association between clinical states ("up" or "down") of disease signatures and clinical effects ("up", "down" or "association") of drugs. A total of 717 clinical variables in the electronic clinical dataset (NHANES), are considered in this study. RESULTS: The statistical significance of our prediction results is supported through two benchmark datasets (Comparative Toxicogenomics Database and Clinical Trials). We discovered not only lots of known relationships between diseases and drugs, but also many hidden disease-drug relationships. For example, glutathione and edetic-acid may be investigated as candidate drugs for asthma treatment. We examined prediction results by using statistical experiments (enrichment verification, hyper-geometric and permutation test P<0.009 in Comparative Toxicogenomics Database and Clinical Trials) and presented evidences for those with already published literature. CONCLUSION: The results show that electronic clinical information is a feasible data resource and utilizing the complementarity (anti-correlated relationships) between clinical signatures of disease and clinical effects of drugs is a potentially predictive concept in drug repositioning research. It makes the proposed approach useful to identity novel relationships between diseases and drugs that have a high probability of being biologically valid.

Role of Atypical Pathogens and the Antibiotic Prescription Pattern in Acute Bronchitis: A Multicenter Study in Korea.

The role of atypical bacteria and the effect of antibiotic treatments in acute bronchitis are still not clear. This study was conducted at 22 hospitals (17 primary care clinics and 5 university hospitals) in Korea. Outpatients (aged ≥ 18 yr) who had an acute illness with a new cough and sputum (≤ 30 days) were enrolled in 2013. Multiplex real-time polymerase chain reaction (RT-PCR) was used to detect five atypical bacteria. A total of 435 patients were diagnosed as having acute bronchitis (vs. probable pneumonia, n = 75), and 1.8% (n = 8) were positive for atypical pathogens (Bordetella pertussis, n = 3; B. parapertussis, n = 0; Mycoplasma pneumoniae, n = 1; Chlamydophila pneumoniae, n = 3; Legionella pneumophila, n = 1). Among clinical symptoms and signs, only post-tussive vomiting was more frequent in patients with atypical pathogens than those without (P = 0.024). In all, 72.2% of the enrolled patients received antibiotic treatment at their first visits, and ß-lactams (29.4%) and quinolones (20.5%) were the most commonly prescribed agents. In conclusion, our study demonstrates that the incidence of atypical pathogens is low in patients with acute bronchitis, and the rate of antibiotic prescriptions is high.

Interpretable Prediction of SARS-CoV-2 Epitope-Specific TCR Recognition Using a Pre-Trained Protein Language Model.

The emergence of the novel coronavirus, designated as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has posed a significant threat to public health worldwide. There has been progress in reducing hospitalizations and deaths due to SARS-CoV-2. However, challenges stem from the emergence of SARS-CoV-2 variants, which exhibit high transmission rates, increased disease severity, and the ability to evade humoral immunity. Epitope-specific T-cell receptor (TCR) recognition is key in determining the T-cell immunogenicity for SARS-CoV-2 epitopes. Although several data-driven methods for predicting epitope-specific TCR recognition have been proposed, they remain challenging due to the enormous diversity of TCRs and the lack of available training data. Self-supervised transfer learning has recently been proven useful for extracting information from unlabeled protein sequences, increasing the predictive performance of fine-tuned models, and using a relatively small amount of training data. This study presents a deep-learning model generated by fine-tuning pre-trained protein embeddings from a large corpus of protein sequences. The fine-tuned model showed markedly high predictive performance and outperformed the recent Gaussian process-based prediction model. The output attentions captured by the deep-learning model suggested critical amino acid positions in the SARS-CoV-2 epitope-specific TCRß sequences that are highly associated with the viral escape of T-cell immune response.

Variation in the total lengths of abutment/implant assemblies generated with a function of applied tightening torque in external and internal implant-abutment connection.

AIM: Settling (embedment relaxation), which is the main cause for screw loosening, is developed by microroughness between implant and abutment metal surface. The objective of this study was to evaluate and compare the relationship between the level of applied torque and the settling of abutments into implants in external and internal implant-abutment connection. MATERIAL AND METHODS: Five different implant-abutment connections were used (Ext, External butt joint + two-piece abutment; Int-H2, Internal hexagon + two-piece abutment; Int-H1, Internal hexagon + one-piece abutment; Int-O2, Internal octagon + two-piece abutment; Int-O1, Internal octagon + one-piece abutment). All abutments of each group were assembled and tightened with corresponding implants by a digital torque gauge. The total lengths of implant-abutment samples were measured at each torque (5, 10, 30 N cm and repeated 30 N cm with 10-min interval) by an electronic digital micrometer. The settling values were calculated by changes between the total lengths of implant-abutment samples. RESULTS: All groups developed settling with repeated tightening. The Int-H2 group showed markedly higher settling for all instances of tightening torque and the Ext group was the lowest. Statistically significant differences were found in settling values between the groups and statistically significant increases were observed within each group at different tightening torques (P<0.05). After the second tightening of 30 N cm, repeated tightening showed almost constant settling values. CONCLUSIONS: Results from the present study suggested that to minimize the settling effect, abutment screws should be retightened at least twice at 30 N cm torque at a 10-min interval in all laboratory and clinical procedures.

Axial Displacements and Removal Torque Changes of Five Different Implant-Abutment Connections under Static Vertical Loading.

The aim of this study was to examine the settling of abutments into implants and the removal torque value under static loading. Five different implant-abutment connections were selected (Ext: external butt joint + two-piece abutment; Int-H2: internal hexagon + two-piece abutment; Int-H1: internal hexagon + one-piece abutment; Int-O2: internal octagon + two-piece abutment; Int-O1: internal octagon + one-piece abutment). Ten implant-abutment assemblies were loaded vertically downward with a 700 N load cell at a displacement rate of 1 mm/min in a universal testing machine. The settling of the abutment was obtained from the change in the total length of the entire implant-abutment unit before and after loading using an electronic digital micrometer. The post-loading removal torque value was compared to the initial torque value with a digital torque gauge. The settling values and removal torque values after 700 N static loading were in the following order, respectively: Ext < Int-H1, Int-H2 < Int-O2 < Int-O1 and Int-O2 < Int-H2 < Ext < Int-H1, Int-O1 (α = 0.05). After 700 N vertical static loading, the removal torque values were statistically different from the initial values, and the post-loading values increased in the Int-O1 group and Int-H1 group (α = 0.05) and decreased in the Ext group, Int-H2 group, and Int-O2 group (α = 0.05). On the basis of the results of this study, it should be taken into consideration that a loss of the preload due to the settling effect can lead to screw loosening during a clinical procedure in the molar region where masticatory force is relatively greater.

A Study of Sofa-Type Capacitive Coupling Electrocardiograph System to Measure Stress Relief for Sleeping or Resting with Oxygen Taking.

Non-contact electrode type electrocardiograph system was researched in various fields, but few products are commercially available. In this study, we proposed a sofa-type capacitive coupling electrocardiograph which has possibilities to be commercialized. The base system is a commercially available oval shape sofa that provides oxygen. We developed a capacitive coupling electrocardiograph and embedded it into the base system. The system gives feedback by measuring the ECG signal on stress relief during resting with taking oxygen provided by the sofa. In the capacitive coupling electrocardiograph, it is inevitable to develop a suitable active electrode for the target system, so we developed that comprised of a surface electrode, electronics, and metal case. The surface electrode was made of PCB with two layers of copper plate: the top layer is for coupling function (coated with AU), and the bottom layer plays a role as a shield. The fabricated active electrode module is embedded into the sofa. The purpose of the developed electrocardiograph is to measure HRV of sofa users. The measured HRV was compared with that from a reference system by various coupling distances (cotton cloth thickness) to guarantee the quality of measured signals. The comparison result shows that RRI correlation was mostly over 0.99, SDNN variation rate was mostly under 1%, and LF/HF variation rate was less than 1% in the tested thicknesses.

Performance study of the wearable one-lead wireless electrocardiographic monitoring system.

This study attempts to compare and assess the performance of a wearable electrocardiogram (ECG) using a sensing fabric electrode and a Bluetooth network with a conventional ECG. A one-lead ECG examination was performed using Bioshirt and an iWorx 214 while walking or running at 3, 6, and 9 km per hour. A correlation coefficient of a heart rate variability (HRV) between these two devices was higher than 0.96 and power spectral density of HRV measured also showed an excellent agreement. Thus, both of these two ECG devices showed similar detection capability for R peaks. The measured values for wave duration and intervals of both devices concur with each other. The intensity of noise is controversial. The ECG device using a sensing fabric electrode and a wireless network showed an ECG signal detection and transmission capability similar to that of a conventional ECG device.

Deep learning-based classification with improved time resolution for physical activities of children.

BACKGROUND: The proportion of overweight and obese people has increased tremendously in a short period, culminating in a worldwide trend of obesity that is reaching epidemic proportions. Overweight and obesity are serious issues, especially with regard to children. This is because obese children have twice the risk of becoming obese as adults, as compared to non-obese children. Nowadays, many methods for maintaining a caloric balance exist; however, these methods are not applicable to children. In this study, a new approach for helping children monitor their activities using a convolutional neural network (CNN) is proposed, which is applicable for real-time scenarios requiring high accuracy. METHODS: A total of 136 participants (86 boys and 50 girls), aged between 8.5 years and 12.5 years (mean 10.5, standard deviation 1.1), took part in this study. The participants performed various movement while wearing custom-made three-axis accelerometer modules around their waists. The data acquired by the accelerometer module was preprocessed by dividing them into small sets (128 sample points for 2.8 s). Approximately 183,600 data samples were used by the developed CNN for learning to classify ten physical activities : slow walking, fast walking, slow running, fast running, walking up the stairs, walking down the stairs, jumping rope, standing up, sitting down, and remaining still. RESULTS: The developed CNN classified the ten activities with an overall accuracy of 81.2%. When similar activities were merged, leading to seven merged activities, the CNN classified activities with an overall accuracy of 91.1%. Activity merging also improved performance indicators, for the maximum case of 66.4% in recall, 48.5% in precision, and 57.4% in f1 score . The developed CNN classifier was compared to conventional machine learning algorithms such as the support vector machine, decision tree, and k-nearest neighbor algorithms, and the proposed CNN classifier performed the best: CNN (81.2%) > SVM (64.8%) > DT (63.9%) > kNN (55.4%) (for ten activities); CNN (91.1%) > SVM (74.4%) > DT (73.2%) > kNN (65.3%) (for the merged seven activities). DISCUSSION: The developed algorithm distinguished physical activities with improved time resolution using short-time acceleration signals from the physical activities performed by children. This study involved algorithm development, participant recruitment, IRB approval, custom-design of a data acquisition module, and data collection. The self-selected moving speeds for walking and running (slow and fast) and the structure of staircase degraded the performance of the algorithm. However, after similar activities were merged, the effects caused by the self-selection of speed were reduced. The experimental results show that the proposed algorithm performed better than conventional algorithms. Owing to its simplicity, the proposed algorithm could be applied to real-time applicaitons.

Rh(0)/Rh(iii) core-shell nanoparticles as heterogeneous catalysts for cyclic carbonate synthesis.

Rh(0)/Rh(iii) core-shell nanoparticles were prepared by surface oxidation of Rh nanoparticles with N-bromosuccinimide. They were employed as heterogeneous catalysts for cyclic carbonate synthesis from propylene oxide and CO2, and exhibited high activity and excellent recyclability due to Lewis acidic Rh(iii) species on the shells.

Settling of abutments into implants and changes in removal torque in five different implant-abutment connections. Part 1: Cyclic loading.

PURPOSE: The aim of this study was to evaluate and compare the settling of abutments into implants and the removal torque values (RTVs) before and after cyclic loading. MATERIALS AND METHODS: Five different implant-abutment connections were tested: Ext = external butt joint + two-piece abutment; Int-H2 = internal hexagon + two-piece abutment; Int-H1 = internal hexagon + one-piece abutment; Int-O2 = internal octagon + two-piece abutment; and Int-O1 = internal octagon + one-piece abutment. Ten abutments from each group were secured to their corresponding implants (total n = 50). All samples were tested in a universal testing machine with a vertical load of 250 N for 100,000 cycles of 14 Hz. The amount of settling of the abutment into the implant was calculated from the change in the total length of the implant-abutment sample before and after loading, as measured with an electronic digital micrometer. The RTV after cyclic loading was compared to the initial RTV with a digital torque gauge. Statistical analysis was performed at a 5% significance level. RESULTS: A multiple-comparison test showed specific significant differences in settling values in each group after 250 N cyclic loading (Int-H1, Ext < Int-H2 < Int-O2 < Int-O1). There were statistically significant decreases in RTVs after loading compared to the initial RTVs in the Int-H2 and Int-O2 groups. No statistically significant differences were found in the Ext, Int-H1, and Int-O1 groups. CONCLUSION: The results of this study demonstrated that the settling amount and RTV (loss of preload) after cyclic loading were specific to the abutment type and related to the design characteristics of the implant-abutment connection.

A highly Lewis-acidic Pd(IV) surface on Pd@SiO2 nanocatalysts for hydroalkoxylation reactions.

The Pd(iv) species, known to be critical intermediates in homogeneous catalysis, were successfully generated on the surface of Pd nanocatalysts via oxidation with iodobenzene dichloride (PhICl2) or N-chlorosuccinimide (NCS). In particular, the Pd@SiO2 yolk-shell nanocatalyst activated by PhICl2 showed high reactivity and superior stability to the other Pd-based catalysts for hydroalkoxylation reactions even at 25 °C.

Controlling helical chirality of cobalt complexes by chirality transfer from vicinal diamines.

Stereoselective cis-trans isomerism of a Co(III)-N2O2 complex has been achieved by coordinating chiral vicinal diamines. The induced metal-centered helical chirality can be exploited for chirality amplification and asymmetric coordination chemistry.

A combination of green tea extract and l-theanine improves memory and attention in subjects with mild cognitive impairment: a double-blind placebo-controlled study.

A combination of green tea extract and l-theanine (LGNC-07) has been reported to have beneficial effects on cognition in animal studies. In this randomized, double-blind, placebo-controlled study, the effect of LGNC-07 on memory and attention in subjects with mild cognitive impairment (MCI) was investigated. Ninety-one MCI subjects whose Mini Mental State Examination-K (MMSE-K) scores were between 21 and 26 and who were in either stage 2 or 3 on the Global Deterioration Scale were enrolled in this study. The treatment group (13 men, 32 women; 57.58 ± 9.45 years) took 1,680 mg of LGNC-07, and the placebo group (12 men, 34 women; 56.28 ± 9.92 years) received an equivalent amount of maltodextrin and lactose for 16 weeks. Neuropsychological tests (Rey-Kim memory test and Stroop color-word test) and electroencephalography were conducted to evaluate the effect of LGNC-07 on memory and attention. Further analyses were stratified by baseline severity to evaluate treatment response on the degree of impairment (MMSE-K 21-23 and 24-26). LGNC-07 led to improvements in memory by marginally increasing delayed recognition in the Rey-Kim memory test (P = .0572). Stratified analyses showed that LGNC-07 improved memory and selective attention by significantly increasing the Rey-Kim memory quotient and word reading in the subjects with MMSE-K scores of 21-23 (LGNC-07, n = 11; placebo, n = 9). Electroencephalograms were recorded in 24 randomly selected subjects hourly for 3 hours in eye-open, eye-closed, and reading states after a single dose of LGNC-07 (LGNC-07, n = 12; placebo, n = 12). Brain theta waves, an indicator of cognitive alertness, were increased significantly in the temporal, frontal, parietal, and occipital areas after 3 hours in the eye-open and reading states. Therefore, this study suggests that LGNC-07 has potential as an intervention for cognitive improvement.