Effect of near-infrared spectroscopy on postoperative delirium in cardiac surgery with cardiopulmonary bypass: a systematic review and meta-analysis.

Background: Postoperative delirium (POD) is a common anesthetic side effect in cardiac surgery. However, the role of oxygen saturation monitoring in reducing postoperative delirium has been controversial. Therefore, this meta-analysis aimed to analyze whether NIRS monitoring during cardiac surgery under cardiopulmonary bypass could reduce the incidence of postoperative delirium. Methods: PubMed, Web of Science, Cochrane Library, Embase and China National Knowledge Infrastructure (CNKI) databases were systematically searched using the related keywords for randomized-controlled trials (RCTs) published from their inception to March 16, 2024. This review was conducted by the Preferred Reporting Project and Meta-Analysis Statement (PRISMA) guidelines for systematic review. The primary outcome was postoperative delirium, and the second outcomes included the length of ICU stay, the incidence of kidney-related adverse outcomes, and the incidence of cardiac-related adverse outcomes. Results: The incidence of postoperative delirium could be reduced under the guidance of near-infrared spectroscopy monitoring (OR, 0.657; 95% CI, 0.447-0.965; P = 0.032; I2 = 0%). However, there were no significant differences in the length of ICU stay (SMD, 0.005 days; 95% CI, -0.135-0.146; P = 0.940; I2 = 39.3%), the incidence of kidney-related adverse outcomes (OR, 0.761; 95% CI, 0.386-1.500; P = 0.430; I2 = 0%), and the incidence of the cardiac-related adverse outcomes (OR, 1.165; 95% CI, 0.556-2.442; P = 0.686; I2 = 0%) between the two groups. Conclusion: Near-infrared spectroscopy monitoring in cardiac surgery with cardiopulmonary bypass helps reduce postoperative delirium in patients. Systematic Review Registration: PROSPERO, identifier, CRD42023482675.

Detectability of hemodynamic oscillations in cerebral cortex through functional near-infrared spectroscopy: a simulation study.

Significance: We explore the feasibility of using time-domain (TD) and continuous-wave (CW) functional near-infrared spectroscopy (fNIRS) to monitor brain hemodynamic oscillations during resting-state activity in humans, a phenomenon that is of increasing interest in the scientific and medical community and appears to be crucial to advancing the understanding of both healthy and pathological brain functioning. Aim: Our general object is to maximize fNIRS sensitivity to brain resting-state oscillations. More specifically, we aim to define comprehensive guidelines for optimizing main operational parameters in fNIRS measurements [average photon count rate, measurement length, sampling frequency, and source-detector distance (SSD)]. In addition, we compare TD and CW fNIRS performance for the detection and localization of oscillations. Approach: A series of synthetic TD and CW fNIRS signals were generated by exploiting the solution of the diffusion equation for two different geometries of the probed medium: a homogeneous medium and a bilayer medium. Known and periodical perturbations of the concentrations of oxy- and deoxy-hemoglobin were imposed in the medium, determining changes in its optical properties. The homogeneous slab model was used to determine the effect of multiple measurement parameters on fNIRS sensitivity to oscillatory phenomena, and the bilayer model was used to evaluate and compare the abilities of TD and CW fNIRS in detecting and isolating oscillations occurring at different depths. For TD fNIRS, two approaches to enhance depth-selectivity were evaluated: first, a time-windowing of the photon distribution of time-of-flight was performed, and then, the time-dependent mean partial pathlength (TMPP) method was used to retrieve the hemoglobin concentrations in the medium. Results: In the homogeneous medium case, the sensitivity of TD and CW fNIRS to periodical perturbations of the optical properties increases proportionally with the average photon count rate, the measurement length, and the sampling frequency and approximatively with the square of the SSD. In the bilayer medium case, the time-windowing method can detect and correctly localize the presence of oscillatory components in the TD fNIRS signal, even in the presence of very low photon count rates. The TMPP method demonstrates how to correctly retrieve the periodical variation of hemoglobin at different depths from the TD fNIRS signal acquired at a single SSD. For CW fNIRS, measurements taken at typical SSDs used for short-separation channel regression show notable sensitivity to oscillations occurring in the deep layer, challenging the assumptions underlying this correction method when the focus is on analyzing oscillatory phenomena. Conclusions: We demonstrated that the TD fNIRS technique allows for the detection and depth-localization of periodical fluctuations of the hemoglobin concentrations within the probed medium using an acquisition at a single SSD, offering an alternative to multi-distance CW fNIRS setups. Moreover, we offered some valuable guidelines that can assist researchers in defining optimal experimental protocols for fNIRS studies.

Vibrational features of oxyamines: a comparative study of N,N-diethylhydroxylyamine and N,N-diethylacetyloxyamine.

The ability of oxyamines to undergo homolytic cleavage of the O-C bond, leading to the formation of stable radicals, is widely used in polymerization processes and to prevent oxidative stress in materials. We present a mid and near-infrared spectroscopy study on two model compounds, the commercial N,N-diethylhydroxyloxyamine (C4H11NO) and the non-commercial N,N-diethylacetyloxyamine (C6H13NO2) in the liquid phase. The analysis of the spectra is based on a complete exploration of the conformational space, coupled to harmonic and anharmonic calculations performed using the generalized second-order vibrational perturbation theory (GVPT2) formalism at the B3LYP-D3(BJ)/Def2-TZVP level of calculation and potential energy distribution analysis. In the most stable species out of 25, the three amine chains present an all-anti arrangement, with the carbonyl oxygen atom pointing towards the nitrogen lone pair. The simulated spectra are in overall good agreement with the experimental ones, and suitable for the assignment of the main observed bands. Furthermore, similarities and divergences between the two molecules are discussed.

Comparison of non-volatile compounds of Penaeus vannemei with different drying treatments via multidimensional infrared spectroscopy.

To analyze the effect of various drying treatments (microwave drying (MD), hot air drying (HAD), vacuum drying (VD), and vacuum freeze drying (VFD)) on taste compounds in Penaeus vannamei, relevant indicators such as free amino acids, 5'-nucleotides, and organic acids were performed. Multidimensional infrared spectroscopy (MM-IR) results found that there were notable variations in taste properties of P. vannamei. There were 18 autocorrelation peaks in 3400-900 cm-1 were screened using second-derivative infrared spectroscopy (SD-IR) and two-dimensional correlation infrared spectroscopy (2DCOS-IR). Variations in functional groups were the major contributors to taste profiles. The TAV of glutamic acid (Glu), guanine (GMP), and inosinemonphosphate (IMP) were greater than one and had notable impacts on taste profiles. VD had the highest equivalent umami value, followed by VFD, HAD, and MD. This study may provide a theoretical guide for the production of dried P. vannamei products on an industrial scale.

Oral Mucosa and Saliva Alterations Related to Vape.

OBJECTIVES: Electronic nicotine delivery systems (e-cigarette, pod, and vape) are currently among the tobacco consumption of adolescents and young adults. The aim is to show oral mucosa and saliva alterations related to vape. MATERIAL AND METHODS: A vape-user patient, presenting a white plaque in the posterior region of the hard palate, underwent clinical examination, sialometry, pH evaluation, and excisional biopsy of the white lesion. Molecular changes in saliva and vape liquid were analyzed by vibrational spectroscopy. RESULTS: The histopathological analyses showed hyperparakeratosis without dysplasia. Formaldehyde, ketones, and aromatic hydrocarbon species were identified in e-cig liquid by the FTIR. CONCLUSIONS: The use of vape may be related to the development of hyperkeratotic lesions in the oral mucosa as well as significantly modify the patient's salivary patterns as the vape liquid presents carcinogenic and cytotoxic components in its composition.

Similarity measure method of near-infrared spectrum combined with multi-attribute information.

Due to the high-dimensionality, redundancy, and non-linearity of the near-infrared (NIR) spectra data, as well as the influence of attributes such as producing area and grade of the sample, which can all affect the similarity measure between samples. This paper proposed a t-distributed stochastic neighbor embedding algorithm based on Sinkhorn distance (St-SNE) combined with multi-attribute data information. Firstly, the Sinkhorn distance was introduced which can solve problems such as KL divergence asymmetry and sparse data distribution in high-dimensional space, thereby constructing probability distributions that make low-dimensional space similar to high-dimensional space. In addition, to address the impact of multi-attribute features of samples on similarity measure, a multi-attribute distance matrix was constructed using information entropy, and then combined with the numerical matrix of spectral data to obtain a mixed data matrix. In order to validate the effectiveness of the St-SNE algorithm, dimensionality reduction projection was performed on NIR spectral data and compared with PCA, LPP, and t-SNE algorithms. The results demonstrated that the St-SNE algorithm effectively distinguishes samples with different attribute information, and produced more distinct projection boundaries of sample category in low-dimensional space. Then we tested the classification performance of St-SNE for different attributes by using the tobacco and mango datasets, and compared it with LPP, t-SNE, UMAP, and Fisher t-SNE algorithms. The results showed that St-SNE algorithm had the highest classification accuracy for different attributes. Finally, we compared the results of searching the most similar sample with the target tobacco for cigarette formulas, and experiments showed that the St-SNE had the highest consistency with the recommendation of the experts than that of the other algorithms. It can provide strong support for the maintenance and design of the product formula.

The use of vibrational spectroscopy and supervised machine learning for chemical identification of plastics ingested by seabirds.

Plastic pollution is now ubiquitous in the environment and represents a growing threat to wildlife, who can mistake plastic for food and ingest it. Tackling this problem requires reliable, consistent methods for monitoring plastic pollution ingested by seabirds and other marine fauna, including methods for identifying different types of plastic. This study presents a robust method for the rapid, reliable chemical characterisation of ingested plastics in the 1-50 mm size range using infrared and Raman spectroscopy. We analysed 246 objects ingested by Flesh-footed Shearwaters (Ardenna carneipes) from Lord Howe Island, Australia, and compared the data yielded by each technique: 92 % of ingested objects visually identified as plastic were confirmed by spectroscopy, 98 % of those were low density polymers such as polyethylene, polypropylene, or their copolymers. Ingested plastics exhibit significant spectral evidence of biological contamination compared to other reports, which hinders identification by conventional library searching. Machine learning can be used to identify ingested plastics by their vibrational spectra with up to 93 % accuracy. Overall, we find that infrared is the more effective technique for identifying ingested plastics in this size range, and that appropriately trained machine learning models can be superior to conventional library searching methods for identifying plastics.

Application of adaptive chaotic dung beetle optimization algorithm to near-infrared spectral model transfer.

A new transfer approach was proposed to share calibration models of the hexamethylenetetramine-acetic acid solution for studying hexamethylenetetramine concentration values across different near-infrared (NIR) spectrometers. This approach combines Savitzky-Golay first derivative (S_G_1) and orthogonal signal correction (OSC) preprocessing, along with feature variable optimization using an adaptive chaotic dung beetle optimization (ACDBO) algorithm. The ACDBO algorithm employs tent chaotic mapping and a nonlinear decreasing strategy, enhancing the balance between global and local search capabilities and increasing population diversity to address limitations observed in traditional dung beetle optimization (DBO). Validated using the CEC-2017 benchmark functions, the ACDBO algorithm demonstrated superior convergence speed, accuracy, and stability. In the context of a partial least squares (PLS) regression model for transferring hexamethylenetetramine-acetic acid solutions using NIR spectroscopy, the ACDBO algorithm excelled over alternative methods such as uninformative variable elimination, competitive adaptive reweighted sampling, cuckoo search, grey wolf optimizer, differential evolution, and DBO in efficiency, accuracy of feature variable selection, and enhancement of model predictive performance. The algorithm attained outstanding metrics, including a determination coefficient for the calibration set (Rc2) of 0.99999, a root mean square error for the calibration set (RMSEC) of 0.00195%, a determination coefficient for the validation set (Rv2) of 0.99643, a root mean squared error for the validation set (RMSEV) of 0.03818%, residual predictive deviation (RPD) of 16.72574. Compared to existing OSC, slope and bias correction (S/B), direct standardization (DS), and piecewise direct standardization (PDS) model transfer methods, the novel strategy enhances the accuracy and robustness of model predictions. It eliminates irrelevant background information about the hexamethylenetetramine concentration, thereby minimizing the spectral discrepancies across different instruments. As a result, this approach yields a determination coefficient for the prediction set (Rp2) of 0.96228, a root mean squared error for the prediction set (RMSEP) of 0.12462%, and a relative error rate (RER) of 17.62331, respectively. These figures closely follow those obtained using DS and PDS, which recorded Rp2, RMSEP, and RER values of 0.97505, 0.10135%, 21.67030, and 0.98311, 0.08339%, 26.33552, respectively. Unlike conventional methods such as OSC, S/B, DS, and PDS, this novel approach does not require the analysis of identical samples across different instruments. This characteristic significantly broadens its applicability for model transfer, which is particularly beneficial for transferring specific measurement samples.

Optical Studies of Doped Two-Dimensional Lead Halide Perovskites: Evidence for Rashba-Split Branches in the Conduction Band.

Two-dimensional (2D) hybrid organic/inorganic perovskites are an emerging materials class for optoelectronic and spintronic applications due to strong excitonic absorption and emission, large spin-orbit coupling, and Rashba spin-splitting effects. For many of the envisioned applications, tuning the majority charge carrier (electron or hole) concentration is desirable, but electronic doping of metal-halide perovskites has proven to be challenging. Here, we demonstrate electron injection into the lower-energy branch of the Rashba-split conduction band of 2D phenethylammonium lead iodide by means of n-type molecular doping at room temperature. The molecular dopant, benzyl viologen (BV), is shown to compensate adventitious p-type impurities and can lead to a tunable Fermi level above the conduction band minimum and increased conductivity in intrinsic samples. The doping-induced carrier concentration is monitored by the observation of free-carrier absorption and intraband optical transitions in the infrared spectral range. These optical measurements allow for an estimation of the Rashba splitting energy ER ≈38 ± 4 meV. Photoinduced quantum beating measurements demonstrate that the excess electron density reduces the electron spin g-factor by ca. 6%. This work demonstrates controllable carrier concentrations in hybrid organic/inorganic perovskites and yields potential for room temperature spin control through the Rashba effect.

Convolutional neural networks can detect orthostatic hypotension in Parkinson's disease using resting-state functional near-infrared spectroscopy data.

Neurological disorders such as Parkinson's disease (PD) often adversely affect the vascular system, leading to alterations in blood flow patterns. Functional near-infrared spectroscopy (fNIRS) is used to monitor hemodynamic changes via signal measurement. This study investigated the potential of using resting-state fNIRS data through a convolutional neural network (CNN) to evaluate PD with orthostatic hypotension. The CNN demonstrated significant efficacy in analyzing fNIRS data, and it outperformed the other machine learning methods. The results indicate that judicious input data selection can enhance accuracy by over 85%, while including the correlation matrix as an input further improves the accuracy to more than 90%. This study underscores the promising role of CNN-based fNIRS data analysis in the diagnosis and management of the PD. This approach enhances diagnostic accuracy, particularly in resting-state conditions, and can reduce the discomfort and risks associated with current diagnostic methods, such as the head-up tilt test.

Investigation of the Mechanical Properties of Thermoplastic Materials Influenced by Different Chemicals.

Objective: The quality of orthodontic forces in aligners is mainly influenced by their mechanical properties. At present, there is insufficient information on how environmental factors affect the mechanical function of aligners, and studies have shown that patients do not pay enough attention to removing aligners while eating and drinking. Therefore, in this study, we investigated the effect of different chemicals on the mechanical properties of thermoplastic materials. Methods: In this study, 175 thermoplastic samples from Easy-Vac gasket (3A Medes, Korea) were prepared, and their chemical composition, tensile strength, and hardness before and after exposure to solutions of orange juice, Cola, chlorhexidine mouthwash, and distilled water were measured. One-Way analysis of variance (ANOVA), Tamhane's test, and Tukey's test were used for statistical analysis. Results: The tensile strength of the sheets increased with continuous exposure to orange juice and chlorhexidine mouthwash, and their hardness decreased with continuous exposure to carbonated beverages. There was no change in the chemical composition of the samples after exposure to different chemicals. Conclusion: Although these changes are statistically significant, they do not have a significant effect on the result of aligner performance. Therefore, the only concern is the cariogenicity of orange juice and Cola during treatment with aligners and the administration of chlorhexidine mouthwash.

Characterization and ecological risk assessment of microplastics accumulated in sea water, sand, sediment, shell water and selected tissues of hermit crab of Sundarban Biosphere Reserve.

Sundarban, a Ramsar site of India, has been encountering an ecological threat due to the presence of microplastic (MP) wastes generated from different anthropogenic sources. Clibanarius longitarsus, an intertidal hermit crab of Sundarban Biosphere Reserve, resides within the abandoned shell of a gastropod mollusc, Telescopium telescopium. We characterized and estimated the MP in the gills and gut of hermit crab, as well as in the water present in its occupied gastropod shell. The average microplastic abundance in sea water, sand and sediment were 0.175 ± 0.145 MP L-1, 42 ± 15.03 MP kg-1 and 67.63 ± 24.13 MP kg-1 respectively. The average microplastic load in hermit crab was 1.94 ± 0.59 MP crab-1, with 33.89 % and 66.11 % in gills and gut respectively. Gastropod shell water exhibited accumulation of 1.69 ± 1.43 MP L-1. Transparent and fibrous microplastics were documented as the dominant polymers of water, sand and sediment. Shell water exhibited the prevalence of green microplastics followed by transparent ones. Microscopic examination revealed microplastics with 100-300 µm size categories were dominant across all abiotic compartments. ATR-FTIR and Raman spectroscopy confirmed polyethylene and polypropylene as the prevalent polymers among the five identified polymers of biotic and abiotic components. The target group index indicated green and black as the preferable microplastics of crab. The ecological risk analysis indicated a considerable level of environmental pollution risk in Sundarban and its inhabiting organisms. This important information base may facilitate in developing a strategy of mitigation to limit the MP induced ecological risk at Sundarban Biosphere Reserve.

Predicting subacute ruminal acidosis from milk mid-infrared estimated fatty acids and machine learning on Canadian commercial dairy herds.

Our objective was to validate the possibility of detecting SARA from milk Fourier transform mid-infrared spectroscopy estimated fatty acids (FA) and machine learning. Subacute ruminal acidosis is a common condition in modern commercial dairy herds for which the diagnostic remains challenging due to its symptoms often being subtle, nonexclusive, and not immediately apparent. This observational study aimed at evaluating the possibility of predicting SARA by developing machine learning models to be applied to farm data and to provide an estimated portrait of SARA prevalence in commercial dairy herds. A first data set composed of 488 milk samples of 67 cows (initial DIM = 8.5 ± 6.18; mean ± SD) from 7 commercial dairy farms and their corresponding SARA classification (SARA+ if rumen pH <6.0 for 300 min, else SARA-) was used for the development of machine learning models. Three sets of predictive variables: i) milk major components (MMC), ii) milk FA (MFA), and iii) MMC combined with MFA (MMCFA) were submitted to 3 different algorithms, namely Elastic net (EN), Extreme gradient boosting (XGB), and Partial least squares (PLS), and evaluated using 3 different scenarios of cross-validation. Accuracy, sensitivity, and specificity of the resulting 27 models were analyzed using a linear mixed model. Model performance was not significantly affected by the choice of algorithm. Model performance was improved by including fatty acids estimations (MFA and MMCFA as opposed to MMC alone). Based on these results, one model was selected (algorithm: EN; predictive variables: MMCFA; 60.4, 65.4, and 55.3% of accuracy, sensitivity, and specificity, respectively) and applied to a large data set comprising the first test-day record (milk major components and FA within the first 70 DIM of 211,972 Holstein cows (219,503 samples) collected from 3001 commercial dairy herds. Based on this analysis, the within-herd SARA prevalence of commercial farms was estimated at 6.6 ± 5.29% ranging from 0 to 38.3%. A subsequent linear mixed model was built to investigate the herd-level factors associated to higher within-herd SARA prevalence. Milking system, proportion of primiparous cows, herd size and seasons were all herd-level factors affecting SARA prevalence. Furthermore, milk production was positively, and milk fat yield negatively associated with SARA prevalence. Due to their moderate levels of accuracy, the SARA prediction models developed in our study, using data from continuous pH measurements on commercial farms, are not suitable for diagnostic purpose. However, these models can provide valuable information at the herd level.

Hemodynamics of short-duration light-intensity physical exercise in the prefrontal cortex of children: a functional near-infrared spectroscopy study.

Identifying the types of exercise that enhance cerebral blood flow is crucial for developing exercise programs that enhance cognitive function. Nevertheless, few studies have explored the amount of light-intensity, short-duration exercises that individuals can easily perform on cerebral blood flow, particularly in children. We examined the effects of these exercises on the hemodynamics of the prefrontal cortex (PFC) using functional near-infrared spectroscopy. Participants comprised 41 children (aged 12.1 ± 1.5 years, 37% female) who engaged in seven light-intensity exercises, with each movement performed in two patterns lasting 10 or 20 s. Changes in oxygenated hemoglobin (oxy-Hb) levels at rest and during exercise were compared using analysis of covariance, with sex and age as covariates. Significant increases in oxy-Hb were observed in multiple regions of the PFC during all forms of exercise (including dynamic and twist stretching [66.6%, 8/12 regions, η2 = 0.07-0.27], hand and finger movements [75.0%, 9/12 regions, η2 = 0.07-0.16], and balance exercises (100.0%, 6/6 regions, η2 = 0.13-0.25]), except for static stretching with monotonic movements. This study implies that short-duration, light-intensity exercises, provided that they entail a certain degree of cognitive and/or physical demands, can activate the PFC and increase blood flow.

Cardiac vagal activity changes moderated the association of cognitive and cerebral hemodynamic variations in the prefrontal cortex.

Phasic cardiac vagal activity (CVA), reflecting ongoing, moment-to-moment psychophysiological adaptations to environmental changes, can serve as a predictor of individual difference in executive function, particularly executive performance. However, the relationship between phasic CVA and executive function demands requires further validation because of previous inconsistent findings. Moreover, it remains unclear what types of phasic changes of CVA may be adaptive in response to heightened executive demands. This study used the standard N-back task to induce different levels of working memory (WM) load and combined functional Near-Infrared Spectroscopy (fNIRS) with a multipurpose polygraph to investigate the variations of CVA and its interactions with cognitive and prefrontal responses as executive demands increased in fifty-two healthy young subjects. Our results showed phasic decreases in CVA as WM load increased (t (51) = -3.758, p < 0.001, Cohen's d = 0.526). Furthermore, phasic changes of CVA elicited by increased executive demands moderated the association of cognitive and cerebral hemodynamic variations in the prefrontal cortex (B = 0.038, SE = 0.014, p < 0.05). Specifically, as executive demands increased, individuals with larger phasic CVA withdrawal showed a positive relationship between cognitive and hemodynamic variations in the prefrontal cortex (ß = 0.281, p = 0.031). No such significant relationship was observed in individuals with smaller phasic CVA withdrawal. The current findings demonstrate a decrease in CVA with increasing executive demands and provide empirical support for the notion that a larger phasic CVA withdrawal can be considered adaptive in situations requiring high executive function demands.

Understanding the structures and interactions in gaseous mixtures of water-alcohol by high-resolution infrared spectroscopy.

Interactions of water and chemical or bio-compound have a universal concern and have been extensively studied. For spectroscopic analysis, the complexity and the low resolution of the spectra make it difficult to obtain the spectral features showing the interactions. In this work, the structures and interactions in gaseous water and water-alcohol mixtures were studied using high-resolution infrared (HR-IR) spectroscopy. The spectral features of water clusters of different sizes, including dimer, trimer, tetramer and pentamer, were observed from the measured spectra of the samples in different volume concentrations, and the interactions of water and methanol/ethanol in the mixtures were obtained. In the analysis, a method based on principal component analysis was used to separate the overlapping spectra. In water-alcohol mixtures, when water is less, water molecules tend to interact with the OH groups on the exterior of the alcohol aggregate, and with the increase of water, a water cage forms around the aggregates. Furthermore, the ratio of the molecule number of methanol in the aggregate to that of water in the cage is around 1:2.3, and the ratio for ethanol is about 1:3.2.

Application of artificial intelligence combined with near infrared spectroscopy in the continuous counter-current extraction process of Angelica dahurica formula granules.

The establishment of near infrared (NIR) spectroscopy model mostly relies on chemometrics, and spectral analysis combined with artificial intelligence (AI) provides a new way of thinking for pharmaceutical quality inspection, new algorithms such as back propagation artificial neural networks (BP-ANN) and swarm intelligence optimization algorithms such as sparrow search algorithm (SSA) provide core technical support. In order to explore the application of AI in the pharmaceutical field, in this study, Angelica dahurica formula granules with a relatively complex system were selected as the research object. Quantitative analysis models were established by using partial least squares regression (PLSR) with a micro-NIR spectrometer, and BP-ANN modeling results were compared. For the best PLSR models of six characteristic components in the continuous counter-current extract of Angelica dahurica, R2v of imperatorin was lower than 0.90, and the RPD values of imperatorin, phellopterin, and isoimperatorin were even lower than 1. When the prediction model established by SSA-BP-ANN was used for quantitative analysis, R2v of six components were all higher than 0.92, and the RPD values all higher than 1.5, which proved that the BP-ANN method was better than PLSR. This study confirmed that in the continuous counter-current extraction progress of Angelica dahurica formula granules, the use of micro-NIR spectrometer combined with AI could realize the rapid prediction of the contents of six characteristic components. The comparison results provided a scientific reference for the process analysis and on-line monitoring in the production process of traditional Chinese medicine by micro-NIR spectrometer combined with AI.

Regional brain activity and neural network changes in cognitive-motor dual-task interference: A functional near-infrared spectroscopy study.

Previous neuroimaging studies have reported dual-task interference (DTi) and deterioration of task performance in a cognitive-motor dual task (DT) compared to that in a single task (ST). Greater frontoparietal activity is a neural signature of DTi; nonetheless, the underlying mechanism of cortical network in DTi still remains unclear. This study aimed to investigate the regional brain activity and neural network changes during DTi induced by highly demanding cognitive-motor DT. Thirty-four right-handed healthy young adults performed the spiral-drawing task. They underwent a paced auditory serial addition test (PASAT) simultaneously or independently while their cortical activity was measured using functional near-infrared spectroscopy. Motor performance was determined using the balanced integration score (BIS), a balanced index of drawing speed and precision. The cognitive task of the PASAT was administered with two difficulty levels defined by 1 s (PASAT-1 s) and 2 s (PASAT-2 s) intervals, allowing for the serial addition of numbers. Cognitive performance was determined using the percentage of correct responses. These motor and cognitive performances were significantly reduced during DT, which combined a drawing and a cognitive task at either difficulty level, compared to those in the corresponding ST conditions. The DT conditions were also characterized by significantly increased activity in the right dorsolateral prefrontal cortex (DLPFC) compared to that in the ST conditions. Multivariate Granger causality (GC) analysis of cortical activity in the selected frontoparietal regions of interest further revealed selective top-down causal connectivity from the right DLPFC to the right inferior parietal cortex during DTs. Furthermore, changes in the frontoparietal GC connectivity strength between the PASAT-2 s DT and ST conditions significantly correlated negatively with changes in the percentage of correct responses. Therefore, DTi can occur even in cognitively proficient young adults, and the right DLPFC and frontoparietal network being crucial neural mechanisms underlying DTi. These findings provide new insights into DTi and its underlying neural mechanisms and have implications for the clinical utility of cognitive-motor DTs applied to clinical populations with cognitive decline, such as those with psychiatric and brain disorders.

Neural dynamics of delayed feedback in robot teleoperation: insights from fNIRS analysis.

Introduction: As robot teleoperation increasingly becomes integral in executing tasks in distant, hazardous, or inaccessible environments, operational delays remain a significant obstacle. These delays, inherent in signal transmission and processing, adversely affect operator performance, particularly in tasks requiring precision and timeliness. While current research has made strides in mitigating these delays through advanced control strategies and training methods, a crucial gap persists in understanding the neurofunctional impacts of these delays and the efficacy of countermeasures from a cognitive perspective. Methods: This study addresses the gap by leveraging functional Near-Infrared Spectroscopy (fNIRS) to examine the neurofunctional implications of simulated haptic feedback on cognitive activity and motor coordination under delayed conditions. In a human-subject experiment (N = 41), sensory feedback was manipulated to observe its influences on various brain regions of interest (ROIs) during teleoperation tasks. The fNIRS data provided a detailed assessment of cerebral activity, particularly in ROIs implicated in time perception and the execution of precise movements. Results: Our results reveal that the anchoring condition, which provided immediate simulated haptic feedback with a delayed visual cue, significantly optimized neural functions related to time perception and motor coordination. This condition also improved motor performance compared to the asynchronous condition, where visual and haptic feedback were misaligned. Discussion: These findings provide empirical evidence about the neurofunctional basis of the enhanced motor performance with simulated synthetic force feedback in the presence of teleoperation delays. The study highlights the potential for immediate haptic feedback to mitigate the adverse effects of operational delays, thereby improving the efficacy of teleoperation in critical applications.

Muscle oxygenation and pain in different types of temporomandibular disorders.

OBJECTIVES: Studies exploring variations in peripheral muscle oxygenation and pressure pain thresholds (PPT) of masticatory muscles in individuals with Temporomandibular Disorders (TMDs) are limited. The purpose of this study was to compare variations in peripheral oxygenation of the masseter muscle; PPT of the masseter and temporal muscles and correlate peripheral muscle oxygenation and PPT of the masseter muscle in individuals with different types of TMDs. MATERIALS AND METHODS: Cross-sectional study involving 116 participants classified into three groups: muscle group (MG, n = 32), joint group (JG, n = 30) and muscle-joint group (MJG, n = 54). Individuals aged 26.97 ± 6.93, 68.97% female, 31,03% males were included. All participants were evaluated using the Diagnostic Criteria for Temporomandibular Disorders, Near-infrared spectroscopy (NIRS) for peripheral muscle oxygenation and pressure algometer for PPT. RESULTS: There was no difference in masseter muscle oxygenation among groups. In the masseter muscle, a weakly positive correlation was observed between PPT and variation in tissue saturation index in the MG (rho = 0.365) and JG (rho = 0.317). In addition, the MJG expressed lower PPT (p = 0.004) than JG, demonstrating that MJG had more pain in this muscle. CONCLUSIONS: MJG have lower PPT in the masseter muscle. Although the PPT is dependent on the type of TMDs, the correlation between PPT and oxygenation is weak. All TMDs groups evaluated (MG, JG, MJG) showed hemodynamic similarities of the masseter muscle. CLINICAL RELEVANCE: Understanding pain thresholds and the hemodynamic behavior of the masticatory muscles contributes to a more assertive physiotherapeutic assessment in TMDs, serving as a basis for careful and individualized interventions.