Assessing changes in regional cerebral hemodynamics in adults with a high-density full-head coverage time-resolved near-infrared spectroscopy device.

Significance: Cerebral oximeters have the potential to detect abnormal cerebral blood oxygenation to allow for early intervention. However, current commercial systems have two major limitations: (1) spatial coverage of only the frontal region, assuming that surgery-related hemodynamic effects are global and (2) susceptibility to extracerebral signal contamination inherent to continuous-wave near-infrared spectroscopy (NIRS). Aim: This work aimed to assess the feasibility of a high-density, time-resolved (tr) NIRS device (Kernel Flow) to monitor regional oxygenation changes across the cerebral cortex during surgery. Approach: The Flow system was assessed using two protocols. First, digital carotid compression was applied to healthy volunteers to cause a rapid oxygenation decrease across the ipsilateral hemisphere without affecting the contralateral side. Next, the system was used on patients undergoing shoulder surgery to provide continuous monitoring of cerebral oxygenation. In both protocols, the improved depth sensitivity of trNIRS was investigated by applying moment analysis. A dynamic wavelet filtering approach was also developed to remove observed temperature-induced signal drifts. Results: In the first protocol (28±5 years; five females, five males), hair significantly impacted regional sensitivity; however, the enhanced depth sensitivity of trNIRS was able to separate brain and scalp responses in the frontal region. Regional sensitivity was improved in the clinical study given the age-related reduction in hair density of the patients (65±15 years; 14 females, 13 males). In five patients who received phenylephrine to treat hypotension, different scalp and brain oxygenation responses were apparent, although no regional differences were observed. Conclusions: The Kernel Flow has promise as an intraoperative neuromonitoring device. Although regional sensitivity was affected by hair color and density, enhanced depth sensitivity of trNIRS was able to resolve differences in scalp and brain oxygenation responses in both protocols.

Gender differences in the functional language networks at birth: a resting-state fNIRS study.

Numerous studies reported inconsistent results concerning gender influences on the functional organization of the brain for language in children and adults. However, data for the gender differences in the functional language networks at birth are sparse. Therefore, we investigated gender differences in resting-state functional connectivity in the language-related brain regions in newborns using functional near-infrared spectroscopy. The results revealed that female newborns demonstrated significantly stronger functional connectivities between the superior temporal gyri and middle temporal gyri, the superior temporal gyri and the Broca's area in the right hemisphere, as well as between the right superior temporal gyri and left Broca's area. Nevertheless, statistical analysis failed to reveal functional lateralization of the language-related brain areas in resting state in both groups. Together, these results suggest that the onset of language system might start earlier in females, because stronger functional connectivities in the right brain in female neonates were probably shaped by the processing of prosodic information, which mainly constitutes newborns' first experiences of speech in the womb. More exposure to segmental information after birth may lead to strengthened functional connectivities in the language system in both groups, resulting in a stronger leftward lateralization in males and a more balanced or leftward dominance in females.

Neural processing of speech comprehension in noise predicts individual age using fNIRS-based brain-behavior models.

Speech comprehension in noise depends on complex interactions between peripheral sensory and central cognitive systems. Despite having normal peripheral hearing, older adults show difficulties in speech comprehension. It remains unclear whether the brain's neural responses could indicate aging. The current study examined whether individual brain activation during speech perception in different listening environments could predict age. We applied functional near-infrared spectroscopy to 93 normal-hearing human adults (20 to 70 years old) during a sentence listening task, which contained a quiet condition and 4 different signal-to-noise ratios (SNR = 10, 5, 0, -5 dB) noisy conditions. A data-driven approach, the region-based brain-age predictive modeling was adopted. We observed a significant behavioral decrease with age under the 4 noisy conditions, but not under the quiet condition. Brain activations in SNR = 10 dB listening condition could successfully predict individual's age. Moreover, we found that the bilateral visual sensory cortex, left dorsal speech pathway, left cerebellum, right temporal-parietal junction area, right homolog Wernicke's area, and right middle temporal gyrus contributed most to prediction performance. These results demonstrate that the activations of regions about sensory-motor mapping of sound, especially in noisy conditions, could be sensitive measures for age prediction than external behavior measures.

Fungal fermentation of Fuzhuan brick tea: A comprehensive evaluation of sensory properties using chemometrics, visible near-infrared spectroscopy, and electronic nose.

Fuzhuan brick tea (FBT) fungal fermentation is a key factor in achieving its unique dark color, aroma, and taste. Therefore, it is essential to develop a rapid and reliable method that could assess its quality during FBT fermentation process. This study focused on using electronic nose (e-nose) and spectroscopy combination with sensory evaluations and physicochemical measurements for building machine learning (ML) models of FBT. The results showed that the fused data achieved 100 % accuracy in classifying the FBT fermentation process. The SPA-MLR method was the best prediction model for FBT quality (R2 = 0.95, RMSEP = 0.07, RPD = 4.23), and the fermentation process was visualized. Where, it was effectively detecting the degree of fermentation relationship with the quality characteristics. In conclusion, the current study's novelty comes from the established real-time method that could sensitively detect the unique post-fermentation quality components based on the integration of spectral, and e-nose and ML approaches.

Near-infrared spectroscopy to quantify overall thermal process intensity during high-moisture extrusion of soy protein concentrate.

High-moisture extrusion (HME) is widely used to produce meat analogues. During HME the plant-based materials experience thermal and mechanical stresses. It is complicated to separate their effects on the final products because these effects are interrelated. In this study we hypothesize that the intensity of the thermal treatment can explain a large part of the physicochemical changes that occur during extrusion. For this reason, near-infrared (NIR) spectroscopy was used as a novel method to quantify the thermal process intensity during HME. High-temperature shear cell (HTSC) processing was used to create a partial least squares (PLS) regression curve for processing temperature under controlled processing conditions (root mean standard error of cross-validation (RMSECV) = 4.00 °C, coefficient of determination of cross-validation (R2CV) = 0.97). This PLS regression model was then applied to HME extrudates produced at different screw speeds (200-1200 rpm) and barrel temperatures (100-160 °C) with two different screw profiles to calculate the equivalent shear cell temperature as a measure for thermal process intensity. This equivalent shear cell temperature reflects the effects of changes in local temperature conditions, residence time and thermal stresses. Furthermore, it can be related to the degree of texturization of the extrudates. This information can be used to gain new insights into the effect of various process parameters during HME on the thermal process intensity and extrudate quality.

Non-invasive prediction of maca powder adulteration using a pocket-sized spectrophotometer and machine learning techniques.

Discriminating different cultivars of maca powder (MP) and detecting their authenticity after adulteration with potent adulterants such as maize and soy flour is a challenge that has not been studied with non-invasive techniques such as near infrared spectroscopy (NIRS). This study developed models to rapidly classify and predict 0, 10, 20, 30, 40, and 50% w/w of soybean and maize flour in red, black and yellow maca cultivars using a handheld spectrophotometer and chemometrics. Soy and maize adulteration of yellow MP was classified with better accuracy than in red MP, suggesting that red MP may be a more susceptible target for adulteration. Soy flour was discovered to be a more potent adulterant compared to maize flour. Using 18 different pretreatments, MP could be authenticated with R2CV in the range 0.91-0.95, RMSECV 6.81-9.16 g/,100 g and RPD 3.45-4.60. The results show the potential of NIRS for monitoring Maca quality.

Charitable crowdfunding donation-intention estimation depending on emotional project images using fNIRS-based functional connectivity.

Charitable fundraising increasingly relies on online crowdfunding platforms. Project images of charitable crowdfunding use emotional appeals to promote helping behavior. Negative emotions are commonly used to motivate helping behavior because the image of a happy child may not motivate donors to donate as willingly. However, some research has found that happy images can be more beneficial. These contradictory results suggest that the emotional valence of project imagery and how fundraisers frame project images effectively remain debatable. Thus, we compared and analyzed brain activation differences in the prefrontal cortex governing human emotions depending on donation decisions using functional near-infrared spectroscopy, a neuroimaging device. We advance existing theory on charitable behavior by demonstrating that little correlation exists in donation intentions and brain activity between negative and positive project images, which is consistent with survey results on donation intentions by victim image. We also discovered quantitative brain hemodynamic signal variations between donors and nondonors, which can predict and detect donor mental brain functioning using functional connectivity, that is, the statistical dependence between the time series of electrophysiological activity and oxygenated hemodynamic levels in the prefrontal cortex. These findings are critical in developing future marketing strategies for online charitable crowdfunding platforms, especially project images.

Non-Invasive Monitoring of Microvascular Oxygenation and Reactive Hyperemia using Hybrid, Near-Infrared Diffuse Optical Spectroscopy for Critical Care.

The detection of levels of impairment in microvascular oxygen consumption and reactive hyperemia is vital in critical care. However, there are no practical means for a robust and quantitative evaluation. This paper describes a protocol to evaluate these impairments using a hybrid near-infrared diffuse optical device. The device contains modules for near-infrared time-resolved and diffuse correlation spectroscopies and pulse-oximetry. These modules allow the non-invasive, continuous, and real-time measurement of the absolute, microvascular blood/tissue oxygen saturation (StO2) and the blood flow index (BFI) along with the peripheral arterial oxygen saturation (SpO2). This device uses an integrated, computer-controlled tourniquet system to execute a standardized protocol with optical data acquisition from the brachioradialis muscle. The standardized vascular occlusion test (VOT) takes care of the variations in the occlusion duration and pressure reported in the literature, while the automation minimizes inter-operator differences. The protocol we describe focuses on a 3-min occlusion period but the details described in this paper can readily be adapted to other durations and cuff pressures, as well as other muscles. The inclusion of an extended baseline and post-occlusion recovery period measurement allows the quantification of the baseline values for all the parameters and the blood/tissue deoxygenation rate that corresponds to the metabolic rate of oxygen consumption. Once the cuff is released, we characterize the tissue reoxygenation rate, magnitude, and duration of the hyperemic response in BFI and StO2. These latter parameters correspond to the quantification of the reactive hyperemia, which provides information about the endothelial function. Furthermore, the above-mentioned measurements of the absolute concentration of oxygenated and deoxygenated hemoglobin, BFI, the derived metabolic rate of oxygen consumption, StO2, and SpO2 provide a yet-to-be-explored rich data set that can exhibit disease severity, personalized therapeutics, and management interventions.

Determination of fumonisin content in maize using near-infrared hyperspectral imaging (NIR-HSI) technology and chemometric methods.

Maize (Zea mays L.) is of socioeconomic importance as an essential food for human and animal nutrition. However, cereals are susceptible to attack by mycotoxin-producing fungi, which can damage health. The methods most commonly used to detect and quantify mycotoxins are expensive and time-consuming. Therefore, alternative non-destructive methods are required urgently. The present study aimed to use near-infrared spectroscopy with hyperspectral imaging (NIR-HSI) and multivariate image analysis to develop a rapid and accurate method for quantifying fumonisins in whole grains of six naturally contaminated maize cultivars. Fifty-eight samples, each containing 40 grains, were subjected to NIR-HSI. These were subsequently divided into calibration (38 samples) and prediction sets (20 samples) based on the multispectral data obtained. The averaged spectra were subjected to various pre-processing techniques (standard normal variate (SNV), first derivative, or second derivative). The most effective pre-treatment performed on the spectra was SNV. Partial least squares (PLS) models were developed to quantify the fumonisin content. The final model presented a correlation coefficient (R2) of 0.98 and root mean square error of calibration (RMSEC) of 508 µg.kg-1 for the calibration set, an R2 of 0.95 and root mean square error of prediction (RMSEP) of 508 µg.kg-1 for the test validation set and a ratio of performance to deviation of 4.7. It was concluded that NIR-HSI with partial least square regression is a rapid, effective, and non-destructive method to determine the fumonisin content in whole maize grains.

NIRis: A low-cost, versatile imaging system for near-infrared fluorescence detection of phototrophic cell colonies used in research and education.

A variety of costly research-grade imaging devices are available for the detection of spectroscopic features. Here we present an affordable, open-source and versatile device, suitable for a range of applications. We provide the files to print the imaging chamber with commonly available 3D printers and instructions to assemble it with easily available hardware. The imager is suitable for rapid sample screening in research, as well as for educational purposes. We provide details and results for an already proven set-up which suits the needs of a research group and students interested in UV-induced near-infrared fluorescence detection of microbial colonies grown on Petri dishes. The fluorescence signal confirms the presence of bacteriochlorophyll a in aerobic anoxygenic phototrophic bacteria (AAPB). The imager allows for the rapid detection and subsequent isolation of AAPB colonies on Petri dishes with diverse environmental samples. To this date, 15 devices have been build and more than 7000 Petri dishes have been analyzed for AAPB, leading to over 1000 new AAPB isolates. Parts can be modified depending on needs and budget. The latest version with automated switches and double band pass filters costs around 350€ in materials and resolves bacterial colonies with diameters of 0.5 mm and larger. The low cost and modular build allow for the integration in high school classes to educate students on light properties, fluorescence and microbiology. Computer-aided design of 3D-printed parts and programming of the employed Raspberry Pi computer could be incorporated in computer sciences classes. Students have been also inspired to do agar art with microbes. The device is currently used in seven different high schools in Finland. Additionally, a science education network of Finnish universities has incorporated it in its program for high school students. Video guides have been produced to facilitate easy operation and accessibility of the device.

Capture of emotional responses under a simulated earthquake experience using near-infrared spectroscopy and virtual reality.

AIM: In a previous study, we reported that watching two-dimensional videos of earthquakes significantly reduced sympathetic nerve activity in healthy young adults. In the present study, we aimed to investigate the emotional responses to earthquakes using immersive virtual reality (VR), which can provide a more realistic experience. METHODS: In total, 24 healthy young adults (12 males, 21.4 ± 0.2 years old) participated. Participants were required to watch earthquake and neutral videos while wearing a head-mounted display and near-infrared spectroscopy (NIRS), during which physiological signals, including pulse rate and cerebral blood flow (CBF) in the dorsolateral prefrontal cortex, were measured. We also analyzed changes in sympathetic and parasympathetic indices and obtained seven emotion ratings: valence, arousal, dominance, fear, astonishment, anxiety, and panic. RESULTS: The VR earthquake videos evoked negative subjective emotions, and the pulse rate significantly decreased. Sympathetic nerve activity tended to decrease, whereas CBF in the left prefrontal cortex showed a slight increase, although this was not significant. CONCLUSIONS: This study showed that measurements combined with NIRS and immersive VR have the potential to capture emotional responses to different stimuli.

Opening the dialogue: A preliminary exploration of hair color, hair cleanliness, light, and motion effects on fNIRS signal quality.

INTRODUCTION: Functional near-infrared spectroscopy (fNIRS) is a promising tool for studying brain activity, offering advantages such as portability and affordability. However, challenges in data collection persist due to factors like participant physiology, environmental light, and gross-motor movements, with limited literature on their impact on fNIRS signal quality. This study addresses four potentially influential factors-hair color, hair cleanliness, environmental light, and gross-motor movements-on fNIRS signal quality. Our aim is to raise awareness and offer insights for future fNIRS research. METHODS: Six participants (4 Females, 2 Males) took part in four different experiments investigating the effects of hair color, hair cleanliness, environmental light, and gross-motor movements on fNIRS signal quality. Participants in Experiment 1, categorized by hair color, completed a finger-tapping task in a between-subjects block design. Signal quality was compared between each hair color. Participants in Experiments 2 and 3 completed a finger-tapping task in a within-subjects block design, with signal quality being compared across hair cleanliness (i.e., five consecutive days without washing the hair) and environmental light (i.e., sunlight, artificial light, no light, etc.), respectively. Experiment 4 assessed three gross-motor movements (i.e., walking, turning and nodding the head) in a within-subjects block design. Motor movements were then compared to resting blocks. Signal quality was evaluated using Scalp Coupling Index (SCI) measurements. RESULTS: Lighter hair produced better signals than dark hair, while the impact of environmental light remains uncertain. Hair cleanliness showed no significant effects, but gross motor movements notably reduced signal quality. CONCLUSION: Our results suggest that hair color, environmental light, and gross-motor movements affect fNIRS signal quality while hair cleanliness does not. Nevertheless, future studies with larger sample sizes are warranted to fully understand these effects. To advance future research, comprehensive documentation of participant demographics and lab conditions, along with signal quality analyses, is essential.

Cerebral cortex activation and functional connectivity during low-load resistance training with blood flow restriction: An fNIRS study.

Despite accumulating evidence that blood flow restriction (BFR) training promotes muscle hypertrophy and strength gain, the underlying neurophysiological mechanisms have rarely been explored. The primary goal of this study is to investigate characteristics of cerebral cortex activity during BFR training under different pressure intensities. 24 males participated in 30% 1RM squat exercise, changes in oxygenated hemoglobin concentration (HbO) in the primary motor cortex (M1), pre-motor cortex (PMC), supplementary motor area (SMA), and dorsolateral prefrontal cortex (DLPFC), were measured by fNIRS. The results showed that HbO increased from 0 mmHg (non-BFR) to 250 mmHg but dropped sharply under 350 mmHg pressure intensity. In addition, HbO and functional connectivity were higher in M1 and PMC-SMA than in DLPFC. Moreover, the significant interaction effect between pressure intensity and ROI for HbO revealed that the regulation of cerebral cortex during BFR training was more pronounced in M1 and PMC-SMA than in DLPFC. In conclusion, low-load resistance training with BFR triggers acute responses in the cerebral cortex, and moderate pressure intensity achieves optimal neural benefits in enhancing cortical activation. M1 and PMC-SMA play crucial roles during BFR training through activation and functional connectivity regulation.

Comparison of LED- and LASER-based fNIRS technologies to record the human peri­spinal cord neurovascular response.

Recently, functional Near-Infrared Spectroscopy (fNIRS) was applied to obtain, non-invasively, the human peri­spinal Neuro-Vascular Response (NVR) under a non-noxious electrical stimulation of a peripheral nerve. This method allowed the measurements of changes in the concentration of oxyhemoglobin (O2Hb) and deoxyhemoglobin (HHb) from the peri­spinal vascular network. However, there is a lack of clarity about the potential differences in perispinal NVR recorded by the different fNIRS technologies currently available. In this work, the two main noninvasive fNIRS technologies were compared, i.e., LED and LASER-based. The recording of the human peri­spinal NVR induced by non-noxious electrical stimulation of a peripheral nerve was recorded simultaneously at C7 and T10 vertebral levels. The amplitude, rise time, and full width at half maximum duration of the perispinal NVRs were characterized in healthy volunteers and compared between both systems. The main difference was that the LED-based system shows about one order of magnitude higher values of amplitude than the LASER-based system. No statistical differences were found for rise time and for duration parameters (at thoracic level). The comparison of point-to-point wave patterns did not show significant differences between both systems. In conclusion, the peri­spinal NRV response obtained by different fNIRS technologies was reproducible, and only the amplitude showed differences, probably due to the power of the system which should be considered when assessing the human peri­spinal vascular network.

Comparison of hyperspectral imaging and spectrometers for prediction of cheeses composition.

Artisanal cheeses are part of the heritage and identity of different countries or regions. In this work, we investigated the spectral variability of a wide range of traditional Brazilian cheeses and compared the performance of different spectrometers to discriminate cheese types and predict compositional parameters. Spectra in the visible (vis) and near infrared (NIR) region were collected, using imaging (vis/NIR-HSI and NIR-HSI) and conventional (NIRS) spectrometers, and it was determined the chemical composition of seven types of cheeses produced in Brazil. Principal component analysis (PCA) showed that spectral variability in the vis/NIR spectrum is related to differences in color (yellowness index) and fat content, while in NIR there is a greater influence of productive steps and fat content. Partial least squares discriminant analysis (PLSDA) models based on spectral information showed greater accuracy than the model based on chemical composition to discriminate types of traditional Brazilian cheeses. Partial least squares (PLS) regression models based on vis/NIR-HSI, NIRS, NIR-HSI data and HSI spectroscopic data fusion (vis/NIR + NIR) demonstrated excellent performance to predict moisture content (RPD > 2.5), good ability to predict fat content (2.0 < RPD < 2.5) and can be used to discriminate between high and low protein values (∼1.5 < RPD < 2.0). The results obtained for imaging and conventional equipment are comparable and sufficiently accurate, so that both can be adapted to predict the chemical composition of the Brazilian traditional cheeses used in this study according to the needs of the industry.

Video mirror feedback induces more extensive brain activation compared to the mirror box: an fNIRS study in healthy adults.

BACKGROUND: Mirror therapy (MT) has been shown to be effective for motor recovery of the upper limb after a stroke. The cerebral mechanisms of mirror therapy involve the precuneus, premotor cortex and primary motor cortex. Activation of the precuneus could be a marker of this effectiveness. MT has some limitations and video therapy (VT) tools are being developed to optimise MT. While the clinical superiority of these new tools remains to be demonstrated, comparing the cerebral mechanisms of these different modalities will provide a better understanding of the related neuroplasticity mechanisms. METHODS: Thirty-three right-handed healthy individuals were included in this study. Participants were equipped with a near-infrared spectroscopy headset covering the precuneus, the premotor cortex and the primary motor cortex of each hemisphere. Each participant performed 3 tasks: a MT task (right hand movement and left visual feedback), a VT task (left visual feedback only) and a control task (right hand movement only). Perception of illusion was rated for MT and VT by asking participants to rate the intensity using a visual analogue scale. The aim of this study was to compare brain activation during MT and VT. We also evaluated the correlation between the precuneus activation and the illusion quality of the visual mirrored feedback. RESULTS: We found a greater activation of the precuneus contralateral to the visual feedback during VT than during MT. We also showed that activation of primary motor cortex and premotor cortex contralateral to visual feedback was more extensive in VT than in MT. Illusion perception was not correlated with precuneus activation. CONCLUSION: VT led to greater activation of a parieto-frontal network than MT. This could result from a greater focus on visual feedback and a reduction in interhemispheric inhibition in VT because of the absence of an associated motor task. These results suggest that VT could promote neuroplasticity mechanisms in people with brain lesions more efficiently than MT. CLINICAL TRIAL REGISTRATION: NCT04738851.

Comparative near Infrared (NIR) spectroscopy calibrations performance of dried and undried forage on dry and wet matter bases.

The application of Near Infrared (NIR) spectroscopy for analyzing wet feed directly on farms is increasingly recognized for its role in supporting harvest-time decisions and refining the precision of animal feeding practices. This study aims to evaluate the accuracy of NIR spectroscopy calibrations for both undried, unprocessed samples and dried, ground samples. Additionally, it investigates the influence of the bases of reference data (wet vs. dry basis) on the predictive capabilities of the NIR analysis. The study utilized 492 Corn Whole Plant (CWP) and 405 High Moisture Corn (HMC) samples, sourced from various farms across Italy. Spectral data were acquired from both undried, unground and dried, ground samples using laboratory bench NIR instruments, covering a spectral range of 1100 to 2498 nm. The reference chemical composition of these samples was analyzed and presented in two formats: on a wet matter basis and on a dry matter basis. The study revealed that calibrations based on undried samples generally exhibited lower predictive accuracy for most traits, with the exception of Dry Matter (DM). Notably, the decline in predictive performance was more pronounced in highly moist products like CWP, where the average error increased by 60-70%. Conversely, this reduction in accuracy was relatively contained (10-15%) in drier samples such as HMC. The Standard Error of Cross-Validation (SECV) values for DMres, Ash, CP, and EE were notably low, at 0.39, 0.30, 0.29, 0.21% for CWP and 0.49, 0.14, 0.25, 0.14% for HMC, respectively. These results align with previous studies, indicating the reliability of NIR spectroscopy in diverse moisture contexts. The study attributes this variance to the interference caused by water in 'as is' samples, where the spectral features predominantly reflect water content, thereby obscuring the spectral signatures of other nutrients. In terms of calibration development strategies, the study concludes that there is no significant difference in predictive performance between undried calibrations based on either 'dry matter' or 'as is' basis. This finding emphasizes the potential of NIR spectroscopy in diverse moisture contexts, although with varying degrees of accuracy contingent upon the moisture content of the analyzed samples. Overall, this research provides valuable insights into the calibration strategies of NIR spectroscopy and its practical applications in agricultural settings, particularly for on-farm forage analysis.

Species-Specific quantification of bioactive boswellic acids in Boswellia resin using NIR spectroscopy, HPLC and Multivariate data analysis.

The bioactive compounds Acetyl-11-keto-ß-boswellic acid (AKBA) and 11-keto-ß-boswellic acid (KBA), found in the resin of the Boswellia tree, exhibit anti-inflammatory properties, rendering Boswellia resin an intriguing natural medicinal products. However, the content of boswellic acids varies across different Boswellia species and proper knowledge of its species-dependent nature, as well as alternatives to the resource- and time-intensive HPLC analysis, are lacking. Here we present a comprehensive investigation into the boswellic acid content of seven Boswellia species from ten countries and introduce a novel and non-destructive Near-Infrared spectroscopy method for predicting boswellic acid concentrations in solid resin samples. The HPLC-UV reference analysis revealed AKBA concentrations of up to 7.27 % (w/w) with KBA concentrations reaching up to 1.28 % (w/w). Principal Component Analysis of the HPLC and NIR spectroscopy data unveiled species-specific variations, facilitating differentiation based on boswellic acid content, characteristic chromatograms and NIR spectra. Using the HPLC-UV quantification as reference, we developed a Partial Least Squares regression model based on NIR spectra of the resin samples. This model demonstrated highly satisfactory predictive capabilities for AKBA content, achieving a root mean square error of prediction of 0.74 % (w/w) and an R2val of 0.79 in independent test set validation. Although the model was less effective for predicting KBA content, it still offered valuable estimates. The spectroscopic method introduced in this study provides a cost-effective and solvent-free approach for predicting boswellic acid content, demonstrating the potential for application in non-laboratory settings through the use of miniaturized NIR spectrometers. Consequently, this method aligns well with the principles of green chemistry and addresses the growing demand for alternative analytical techniques.

Physiologic Determinants of Near-Infrared Spectroscopy-Derived Cerebral and Tissue Oxygen Saturation Measurements in Critically Ill Patients.

OBJECTIVES: Near-infrared spectroscopy (NIRS) is a potentially valuable modality to monitor the adequacy of oxygen delivery to the brain and other tissues in critically ill patients, but little is known about the physiologic determinants of NIRS-derived tissue oxygen saturations. The purpose of this study was to assess the contribution of routinely measured physiologic parameters to tissue oxygen saturation measured by NIRS. DESIGN: An observational sub-study of patients enrolled in the Role of Active Deresuscitation After Resuscitation-2 (RADAR-2) randomized feasibility trial. SETTING: Two ICUs in the United Kingdom. PATIENTS: Patients were recruited for the RADAR-2 study, which compared a conservative approach to fluid therapy and deresuscitation with usual care. Those included in this sub-study underwent continuous NIRS monitoring of cerebral oxygen saturations (SctO2) and quadriceps muscle tissue saturations (SmtO2). INTERVENTION: Synchronized and continuous mean arterial pressure (MAP), heart rate (HR), and pulse oximetry (oxygen saturation, Spo2) measurements were recorded alongside NIRS data. Arterial Paco2, Pao2, and hemoglobin concentration were recorded 12 hourly. Linear mixed effect models were used to investigate the association between these physiologic variables and cerebral and muscle tissue oxygen saturations. MEASUREMENTS AND MAIN RESULTS: Sixty-six patients were included in the analysis. Linear mixed models demonstrated that Paco2, Spo2, MAP, and HR were weakly associated with SctO2 but only explained 7.1% of the total variation. Spo2 and MAP were associated with SmtO2, but together only explained 0.8% of its total variation. The remaining variability was predominantly accounted for by between-subject differences. CONCLUSIONS: Our findings demonstrated that only a small proportion of variability in NIRS-derived cerebral and tissue oximetry measurements could be explained by routinely measured physiologic variables. We conclude that for NIRS to be a useful monitoring modality in critical care, considerable further research is required to understand physiologic determinants and prognostic significance.

Neurovascular Coupling Analysis Based on Multivariate Variational Gaussian Process Convergent Cross-Mapping.

Neurovascular coupling (NVC) provides important insights into the intricate activity of brain functioning and may aid in the early diagnosis of brain diseases. Emerging evidences have shown that NVC could be assessed by the coupling between electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). However, this endeavor presents significant challenges due to the absence of standardized methodologies and reliable techniques for coupling analysis of these two modalities. In this study, we introduced a novel method, i.e., the collaborative multi-output variational Gaussian process convergent cross-mapping (CMVGP-CCM) approach to advance coupling analysis of EEG and fNIRS. To validate the robustness and reliability of the CMVGP-CCM method, we conducted extensive experiments using chaotic time series models with varying noise levels, sequence lengths, and causal driving strengths. In addition, we employed the CMVGP-CCM method to explore the NVC between EEG and fNIRS signals collected from 26 healthy participants using a working memory (WM) task. Results revealed a significant causal effect of EEG signals, particularly the delta, theta, and alpha frequency bands, on the fNIRS signals during WM. This influence was notably observed in the frontal lobe, and its strength exhibited a decline as cognitive demands increased. This study illuminates the complex connections between brain electrical activity and cerebral blood flow, offering new insights into the underlying NVC mechanisms of WM.