Cerebral rScO2 Measured by Near-Infrared Spectroscopy (NIRS) During Therapeutic Hypothermia in Neonates with Hypoxic-Ischemic Encephalopathy: A Systematic Review.

INTRODUCTION: Perinatal asphyxia, a leading cause of neonatal mortality and neurological sequelae, necessitates early detection of pathophysiological neurologic changes during hypoxic-ischaemic encephalopathy (HIE). This study aimed to review published data on rScO2 monitoring during hypothermia treatment in neonates with perinatal asphyxia to predict short- and long-term neurological injury. METHODS: A systematic review was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Study identification was performed through a search between November and December 2021 in the electronic databases PubMed, Embase, Lilacs, Scopus, Web of Science, and Cochrane Central Register of Controlled Trials (CENTRAL). The main outcome was short-term (Changes in brain magnetic resonating imaging) and long-term (In neurodevelopment) neurological injury. The study protocol was registered in PROSPERO (International Prospective Register of Systematic Reviews) with CRD42023395438. RESULTS: 380 articles were collected from databases in the initial search. Finally, 15 articles were selected for extraction and analysis of the information. An increase in rScO2 measured by NIRS (Near-infrared spectroscopy) at different moments of treatment predicts neurological injury. However, there exists a wide variability in the methods and outcomes of the studies. CONCLUSION: High rScO2 values were found to predict negative outcomes, with substantial discord among studies. NIRS is proposed as a real-time bedside tool for predicting brain injury in neonates with moderate to severe HIE.

Integrating portable NIR spectrometry with deep learning for accurate Estimation of crude protein in corn feed.

This study investigates the challenges encountered in utilizing portable near-infrared (NIR) spectrometers in agriculture, specifically in developing predictive models with high accuracy and robust generalization abilities despite limited spectral resolution and small sample sizes. The research concentrates on the near-infrared spectra of corn feed, utilizing spectral processing techniques and CNNs to precisely estimate crude protein content. Five preprocessing methods were implemented alongside two-dimensional (2D) correlation spectroscopy, resulting in the development of both one-dimensional (1D) and 2D regression models. A comparative analysis of these models in predicting crude protein content demonstrated that 1D-CNNs exhibited superior predictive performance within the 1D category. For the 2D models, CropNet and CropResNet were utilized, with CropResNet demonstrating more accurate and superior predictive capabilities. Overall, the integration of 2D correlation spectroscopy with suitable preprocessing techniques in deep learning models, particularly the 2D CropResNet, proved to be more precise in predicting the crude protein content in corn feed. This finding emphasis the potential of this approach in the portable spectrometer market.

Measurement of the rotational relaxation time of intracellular water in dried yeast and Jurkat cells by near infrared spectroscopy.

Molecular mobility of intracellular water is a crucial parameter in the study of the mechanism of desiccation tolerance. As one of the parameters that reflecting molecular mobility, the viscosity of intracellular water has been found intimately related with the protection of the phospholipid membrane because it quantifies the diffusion ability of water and mass in the intracellular environment. In this work we measured the intracellular water relaxation time, which can be translated into water viscosity, by using a previously established NIR-dielectric method to monitor the drying process of baker's yeast and Jurkat cells with different desiccation tolerance. We found that intracellular saccharide can significantly decrease the intracellular water viscosity. Also, the intracellular water diffusion coefficient obtained from this method were found in good agreement with other reports.

Predicting ruminal degradability and chemical composition of corn silage using near-infrared spectroscopy and multivariate regression.

The aim of this study was to develop and validate regression models to predict the chemical composition and ruminal degradation parameters of corn silage by near-infrared spectroscopy (NIR). Ninety-four samples were used to develop and validate the models to predict corn silage composition. A subset of 23 samples was used to develop and validate models to predict ruminal degradation parameters of corn silage. Wet chemistry methods were used to determine the composition values and ruminal degradation parameters of the corn silage samples. The dried and ground samples had their NIR spectra scanned using a poliSPECNIR 900-1700 model NIR sprectrophotometer (ITPhotonics S.r.l, Breganze, IT.). The models were developed using regression by partial least squares (PLS), and the ordered predictor selection (OPS) method was used. In general, the regression models obtained to predict the corn silage composition (P>0.05), except the model for organic matter (OM), adequately estimated the studied properties. It was not possible to develop prediction models for the potentially degradable fraction in the rumen of OM and crude protein and the degradation rate of OM. The regression models that could be obtained to predict the ruminal degradation parameters showed correlation coefficient of calibration between 0.530 and 0.985. The regression models developed to predict CS composition accurately estimated the CS composition, except the model for OM. The NIR has potential to be used by nutritionists as a rapid prediction tool for ruminal degradation parameters in the field.

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Further evidence is needed to explore the impact of high-altitude environments on the neurologic function of neonates. Non-invasive techniques such as cerebral near-infrared spectroscopy and amplitude-integrated electroencephalography can provide data on cerebral oxygenation and brain electrical activity. This study will conduct multiple cerebral near-infrared spectroscopy and amplitude-integrated electroencephalography monitoring sessions at various time points within the first 3 days postpartum for healthy full-term neonates at different altitudes. The obtained data on cerebral oxygenation and brain electrical activity will be compared between different altitudes, and corresponding reference ranges will be established. The study involves 6 participating centers in the Chinese High Altitude Neonatal Medicine Alliance, with altitude gradients divided into 4 categories: 800 m, 1 900 m, 2 400 m, and 3 500 m, with an anticipated sample size of 170 neonates per altitude gradient. This multicenter prospective cohort study aims to provide evidence supporting the impact of high-altitude environments on early brain function and metabolism in neonates.

Alterations in brain functional connectivity in patients with mild cognitive impairment: A systematic review and meta-analysis of functional near-infrared spectroscopy studies.

Emerging evidences suggest that cognitive deficits in individuals with mild cognitive impairment (MCI) are associated with disruptions in brain functional connectivity (FC). This systematic review and meta-analysis aimed to comprehensively evaluate alterations in FC between MCI individuals and healthy control (HC) using functional near-infrared spectroscopy (fNIRS). Thirteen studies were included in qualitative analysis, with two studies synthesized for quantitative meta-analysis. Overall, MCI patients exhibited reduced resting-state FC, predominantly in the prefrontal, parietal, and occipital cortex. Meta-analysis of two studies revealed a significant reduction in resting-state FC from the right prefrontal to right occipital cortex (standardized mean difference [SMD] = -.56; p < .001), left prefrontal to left occipital cortex (SMD = -.68; p < .001), and right prefrontal to left occipital cortex (SMD = -.53; p < .001) in MCI patients compared to HC. During naming animal-walking task, MCI patients exhibited enhanced FC in the prefrontal, motor, and occipital cortex, whereas a decrease in FC was observed in the right prefrontal to left prefrontal cortex during calculating-walking task. In working memory tasks, MCI predominantly showed increased FC in the medial and left prefrontal cortex. However, a decreased in prefrontal FC and a shifted in distribution from the left to the right prefrontal cortex were noted in MCI patients during a verbal frequency task. In conclusion, fNIRS effectively identified abnormalities in FC between MCI and HC, indicating disrupted FC as potential markers for the early detection of MCI. Future studies should investigate the use of task- and region-specific FC alterations as a sensitive biomarker for MCI.

A dynamic brain network decomposition method discovers effective brain hemodynamic sub-networks for Parkinson's disease.

Objective.Dopaminergic treatment is effective for Parkinson's disease (PD). Nevertheless, the conventional treatment assessment mainly focuses on human-administered behavior examination while the underlying functional improvements have not been well explored. This paper aims to investigate brain functional variations of PD patients after dopaminergic therapy.Approach.This paper proposed a dynamic brain network decomposition method and discovered brain hemodynamic sub-networks that well characterized the efficacy of dopaminergic treatment in PD. Firstly, a clinical walking procedure with functional near-infrared spectroscopy was developed, and brain activations during the procedure from fifty PD patients under the OFF and ON states (without and with dopaminergic medication) were captured. Then, dynamic brain networks were constructed with sliding-window analysis of phase lag index and integrated time-varying functional networks across all patients. Afterwards, an aggregated network decomposition algorithm was formulated based on aggregated effectiveness optimization of functional networks in spanning network topology and cross-validation network variations, and utilized to unveil effective brain hemodynamic sub-networks for PD patients. Further, dynamic sub-network features were constructed to characterize the brain flexibility and dynamics according to the temporal switching and activation variations of discovered sub-networks, and their correlations with differential treatment-induced gait alterations were analyzed.Results.The results demonstrated that PD patients exhibited significantly enhanced flexibility after dopaminergic therapy within a sub-network related to the improvement of motor functions. Other sub-networks were significantly correlated with trunk-related axial symptoms and exhibited no significant treatment-induced dynamic interactions.Significance.The proposed method promises a quantified and objective approach for dopaminergic treatment evaluation. Moreover, the findings suggest that the gait of PD patients comprises distinct motor domains, and the corresponding neural controls are selectively responsive to dopaminergic treatment.

Reliability Evaluation for Continuous-Wave Functional Near-Infrared Spectroscopy Systems: Comprehensive Testing from Bench Characterization to Human Test.

In recent years, biomedical optics technology has developed rapidly. The current widespread use of biomedical optics was made possible by the invention of optical instruments. The advantages of being non-invasive, portable, effective, low cost, and less susceptible to system noise have led to the rapid development of functional near-infrared spectroscopy (fNIRS) technology for hemodynamics detection, especially in the field of functional brain imaging. At the same time, laboratories and companies have developed various fNIRS-based systems. The safety, stability, and efficacy of fNIRS systems are key performance indicators. However, there is still a lack of comprehensive and systematic evaluation methods for fNIRS instruments. This study uses the fNIRS system developed in our laboratory as the test object. The test method established in this study includes system validation and performance testing to comprehensively assess fNIRS systems' reliability. These methods feature low cost and high practicality. Based on this study, existing or newly developed systems can be comprehensively and easily evaluated in the laboratory or workspace.

Near-InfraRed Spectroscopy Provides a Reproducible Estimate of Muscle Aerobic Capacity, but Not Whole-Body Aerobic Power.

Near-infrared spectroscopy (NIRS) during repeated limb occlusions is a noninvasive tool for assessing muscle oxidative capacity. However, the method's reliability and validity remain under investigation. This study aimed to determine the reliability of the NIRS-derived mitochondrial power of the musculus vastus lateralis and its correlation with whole-body (cycling) aerobic power (V̇O2 peak). Eleven healthy active men (28 ± 10 y) twice (2 days apart) underwent repeated arterial occlusions to induce changes in muscle oxygen delivery after 15 s of electrical muscle stimulation. The muscle oxygen consumption (mV̇O2) recovery time and rate (k) constants were calculated from the NIRS O2Hb signal. We assessed the reliability (coefficient of variation and intraclass coefficient of correlation [ICC]) and equivalency (t-test) between visits. The results showed high reproducibility for the mV̇O2 recovery time constant (ICC = 0.859) and moderate reproducibility for the k value (ICC = 0.674), with no significant differences between visits (p > 0.05). NIRS-derived k did not correlate with the V̇O2 peak relative to body mass (r = 0.441, p = 0.17) or the absolute V̇O2 peak (r = 0.366, p = 0.26). In conclusion, NIRS provides a reproducible estimate of muscle mitochondrial power, which, however, was not correlated with whole-body aerobic capacity in the current study, suggesting that even if somewhat overlapping, not the same set of factors underpin these distinct indices of aerobic capacity at the different (peripheral and whole-body systemic) levels.

Near-Infrared Spectroscopy During Reactive Hyperemia for the Assessment of Lower Limb Vascular Function.

Vascular diseases of the lower limb contribute substantially to the global burden of cardiovascular disease and comorbidities such as diabetes. Importantly, microvascular dysfunction can occur prior to, or alongside, macrovascular pathology, and both potentially contribute to patient symptoms and disease burden. Here, we describe a non-invasive approach using near-infrared spectroscopy (NIRS) during reactive hyperemia, which provides a standardized assessment of lower limb vascular (dys)function and a potential method to evaluate the efficacy of therapeutic interventions. Unlike alternative methods, such as contrast-enhanced ultrasound, this approach does not require venous access or sophisticated image analysis, and it is inexpensive and less operator-dependent. This description of the NIRS method includes representative results and standard terminology alongside the discussion of measurement considerations, limitations, and alternative methods. Future application of this work will improve standardization of vascular research design, data collection procedures, and harmonized reporting, thereby enhancing translational research outcomes in the areas of lower limb vascular (dys)function, disease, and treatment.

Research on the chemical oxygen demand spectral inversion model in water based on IPLS-GAN-SVM hybrid algorithm.

Spectral collinearity and limited spectral datasets are the problems influencing Chemical Oxygen Demand (COD) modeling. To address the first problem and obtain optimal modeling range, the spectra are preprocessed using six methods including Standard Normal Variate, Savitzky-Golay Smoothing Filtering (SG) etc. Subsequently, the 190-350 nm spectral range is divided into 10 subintervals, and Interval Partial Least Squares (IPLS) is used to perform PLS modeling on each interval. The results indicate that it is best modeled in the 7th range (238~253 nm). The values of Mean Square Error (MSE), Mean Absolute Error (MAE) and R2score of the model without pretreatment are 1.6489, 1.0661, and 0.9942. After pretreatment, the SG is better than others, with MSE and MAE decreasing to 1.4727, 1.0318 and R2score improving to 0.9944. Using the optimal model, the predicted COD for three samples are 10.87 mg/L, 14.88 mg/L, and 19.29 mg/L. To address the problem of the small dataset, using Generative Adversarial Networks for data augmentation, three datasets are obtained for Support Vector Machine (SVM) modeling. The results indicate that, compared to the original dataset, the SVM's MSE and MAE have decreased, while its accuracy has improved by 2.88%, 11.53%, and 11.53%, and the R2score has improved by 18.07%, 17.40%, and 18.74%.

[Research on Memory Cognitive Training Based on fNIRS and Neurofeedback].

The study developed a memory task training system using functional near-infrared spectroscopy (fNIRS) and neurofeedback mechanisms, and acquired and analyzed subjects' EEG signals. The results showed that subjects participating in the neurofeedback task had higher correlated brain network node degrees and average cluster coefficients in the right hemisphere brain region of the prefrontal lobe, with relatively lower dispersion of mediator centrality. In addition, the subjects' left hemisphere brain region of the prefrontal lobe section had increased centrality in the neurofeedback task. Classification of brain data by the channel network model and the support vector machine model showed that the classification accuracy of both models was higher in the task state and resting state than in the feedback task and the control task, and the classification accuracy of the channel network model was higher. The results suggested that subjects in the neurofeedback task had distinct brain data features and that these features could be effectively recognized.

Exploring Deep Learning to Predict Coconut Milk Adulteration Using FT-NIR and Micro-NIR Spectroscopy.

Accurately identifying adulterants in agriculture and food products is associated with preventing food safety and commercial fraud activities. However, a rapid, accurate, and robust prediction model for adulteration detection is hard to achieve in practice. Therefore, this study aimed to explore deep-learning algorithms as an approach to accurately identify the level of adulterated coconut milk using two types of NIR spectrophotometer, including benchtop FT-NIR and portable Micro-NIR. Coconut milk adulteration samples came from deliberate adulteration with corn flour and tapioca starch in the 1 to 50% range. A total of four types of deep-learning algorithm architecture that were self-modified to a one-dimensional framework were developed and tested to the NIR dataset, including simple CNN, S-AlexNET, ResNET, and GoogleNET. The results confirmed the feasibility of deep-learning algorithms for predicting the degree of coconut milk adulteration by corn flour and tapioca starch using NIR spectra with reliable performance (R2 of 0.886-0.999, RMSE of 0.370-6.108%, and Bias of -0.176-1.481). Furthermore, the ratio of percent deviation (RPD) of all algorithms with all types of NIR spectrophotometers indicates an excellent capability for quantitative predictions for any application (RPD > 8.1) except for case predicting tapioca starch, using FT-NIR by ResNET (RPD < 3.0). This study demonstrated the feasibility of using deep-learning algorithms and NIR spectral data as a rapid, accurate, robust, and non-destructive way to evaluate coconut milk adulterants. Last but not least, Micro-NIR is more promising than FT-NIR in predicting coconut milk adulteration from solid adulterants, and it is portable for in situ measurements in the future.

Near-infrared hyperspectral imaging and robust statistics for in vivo non-melanoma skin cancer and actinic keratosis characterisation.

One of the most common forms of cancer in fair skinned populations is Non-Melanoma Skin Cancer (NMSC), which primarily consists of Basal Cell Carcinoma (BCC), and cutaneous Squamous Cell Carcinoma (SCC). Detecting NMSC early can significantly improve treatment outcomes and reduce medical costs. Similarly, Actinic Keratosis (AK) is a common skin condition that, if left untreated, can develop into more serious conditions, such as SCC. Hyperspectral imagery is at the forefront of research to develop non-invasive techniques for the study and characterisation of skin lesions. This study aims to investigate the potential of near-infrared hyperspectral imagery in the study and identification of BCC, SCC and AK samples in comparison with healthy skin. Here we use a pushbroom hyperspectral camera with a spectral range of ≈ 900 to 1600 nm for the study of these lesions. For this purpose, an ad hoc platform was developed to facilitate image acquisition. This study employed robust statistical methods for the identification of an optimal spectral window where the different samples could be differentiated. To examine these datasets, we first tested for the homogeneity of sample distributions. Depending on these results, either traditional or robust descriptive metrics were used. This was then followed by tests concerning the homoscedasticity, and finally multivariate comparisons of sample variance. The analysis revealed that the spectral regions between 900.66-1085.38 nm, 1109.06-1208.53 nm, 1236.95-1322.21 nm, and 1383.79-1454.83 nm showed the highest differences in this regard, with <1% probability of these observations being a Type I statistical error. Our findings demonstrate that hyperspectral imagery in the near-infrared spectrum is a valuable tool for analyzing, diagnosing, and evaluating non-melanoma skin lesions, contributing significantly to skin cancer research.

Respiratory and locomotor muscle blood flow measurements using near-infrared spectroscopy and indocyanine green dye in health and disease.

Measuring respiratory and locomotor muscle blood flow during exercise is pivotal for understanding the factors limiting exercise tolerance in health and disease. Traditional methods to measure muscle blood flow present limitations for exercise testing. This article reviews a method utilising near-infrared spectroscopy (NIRS) in combination with the light-absorbing tracer indocyanine green dye (ICG) to simultaneously assess respiratory and locomotor muscle blood flow during exercise in health and disease. NIRS provides high spatiotemporal resolution and can detect chromophore concentrations. Intravenously administered ICG binds to albumin and undergoes rapid metabolism, making it suitable for repeated measurements. NIRS-ICG allows calculation of local muscle blood flow based on the rate of ICG accumulation in the muscle over time. Studies presented in this review provide evidence of the technical and clinical validity of the NIRS-ICG method in quantifying respiratory and locomotor muscle blood flow. Over the past decade, use of this method during exercise has provided insights into respiratory and locomotor muscle blood flow competition theory and the effect of ergogenic aids and pharmacological agents on local muscle blood flow distribution in COPD. Originally, arterial blood sampling was required via a photodensitometer, though the method has subsequently been adapted to provide a local muscle blood flow index using venous cannulation. In summary, the significance of the NIRS-ICG method is that it provides a minimally invasive tool to simultaneously assess respiratory and locomotor muscle blood flow at rest and during exercise in health and disease to better appreciate the impact of ergogenic aids or pharmacological treatments.

Non-invasive blood glucose estimation method based on the phase delay between oxy- and deoxyhemoglobin using visible and near-infrared spectroscopy.

Significance: Many researchers have attempted to estimate blood glucose levels (BGLs) noninvasively using near-infrared (NIR) spectroscopy. However, the optical absorption change induced by blood glucose is weak in the NIR region and often masked by interference from other components such as water and hemoglobin. Aim: Instead of using direct optical absorption by glucose, this study proposes an index calculated from oxy- and deoxyhemoglobin signals that shows a good correlation with BGLs while using conventional visible and NIR spectroscopy. Approach: The metabolic index, which is based on tissue oxygen consumption, was derived through analytical methods and further verified and reproduced in a series of glucose challenge experiments. Blood glucose estimation units were prototyped by utilizing commercially available smart devices. Results: Our experimental results showed that the phase delay between the oxy- and deoxyhemoglobin signals in near-infrared spectroscopy correlates with BGL measured by a conventional continuous glucose monitor. The proposed method was also confirmed to work well with visible spectroscopy systems based on smartphone cameras. The proposed method also demonstrated excellent repeatability in results from a total of 19 oral challenge tests. Conclusions: This study demonstrated the feasibility of non-invasive glucose monitoring using existing photoplethysmography sensors for pulse oximeters and smartwatches. Evaluating the proposed method in diabetic or unhealthy individuals may serve to further increase its practicality.

Relationship between accelerometer-measured sleep duration and Stroop performance: a functional near-infrared spectroscopy study among young adults.

Objectives: Short sleep is becoming more common in modern society. This study aimed to explore the relationship between accelerometer-measured sleep duration and cognitive performance among young adults as well as the underlying hemodynamic mechanisms. Methods: A total of 58 participants were included in this study. Participants were asked to wear an ActiGraph GT3X+ accelerometer to identify their sleep duration for 7 consecutive days. Cognitive function was assessed by the Stroop test. Two conditions, including the congruent and incongruent Stroop, were set. In addition, stratified analyses were used to examine sensitivity. 24-channel functional near-infrared spectroscopy (fNIRS) equipment was applied to measure hemodynamic changes of the prefrontal cortex (PFC) during cognitive tasks. Results: Results showed that sleep duration was positively associated with accuracy of the incongruent Stroop test (0.001 (0.000, 0.002), p = 0.042). Compared with the regular sleep (≥7 h) group, lower accuracy of the incongruent Stroop test (-0.012 (-0.023, -0.002), p = 0.024) was observed in the severe short sleep (<6 h). Moreover, a stratified analysis was conducted to examining gender, age, BMI, birthplace, and education's impact on sleep duration and the incongruent Stroop test accuracy, confirming a consistent correlation across all demographics. In the severe short sleep group, the activation of left middle frontal gyri and right dorsolateral superior frontal gyri were negatively associated with the cognitive performance. Conclusions: This study emphasized the importance of maintaining enough sleep schedules in young college students from a fNIRS perspective. The findings of this study could potentially be used to guide sleep time in young adults and help them make sleep schemes.

An automatic parathyroid recognition and segmentation model based on deep learning of near-infrared autofluorescence imaging.

INTRODUCTION: Near-infrared autofluorescence imaging (NIFI) can be used to identify parathyroid gland (PG) during surgery. The purpose of the study is to establish a new model, help surgeons better identify, and protect PGs. METHODS: Five hundred and twenty three NIFI images were selected. The PGs were recorded by NIFI and marked with artificial intelligence (AI) model. The recognition rate for PGs was calculated. Analyze the differences between surgeons of different years of experience and AI recognition, and evaluate the diagnostic and therapeutic efficacy of AI model. RESULTS: Our model achieved 83.5% precision and 57.8% recall in the internal validation set. The visual recognition rate of AI model was 85.2% and 82.4% on internal and external sets. The PG recognition rate of AI model is higher than that of junior surgeons (p < 0.05). CONCLUSIONS: This AI model will help surgeons identify PGs, and develop their learning ability and self-confidence.

Non-destructive detection of single corn seed vigor based on visible/near-infrared spatially resolved spectroscopy combined with chemometrics.

Seed vigor is an essential quality evaluation index for seed selection. However, accurately detecting the vigor of a single corn seed is challenging. In this study, we constructed a single-fiber spatially resolved detection device using visible/near-infrared spectroscopy to investigate the patterns and correlations between spatially resolved spectroscopy (SRS) at 500-1000 nm and seed vigor. The device collected spectral data at a light source-detector distance of 5-6.6 mm on the embryo side (S1) and endosperm side (S2) of the corn seeds. The proposed spectral ratio method based on SRS and spectral combination analysis achieved an improvement in the detection accuracy of different corn seed vigor. Modeling by SG-CARS-PLSDA using the ratio method showed further improvement in the prediction ability. The highest accuracy for both S1 and S2 in the Zhengdan 958 variety was 91.67 %, while those of S1 and S2 for the Shaandan 650 variety were 86.67 % and 88.33 %, respectively. In addition, SRS was found to be more advantageous in S2 acquisition, verifying the potential of SRS in the non-destructive testing of seed vigor. This provides a favorable reference for the comprehensive evaluation of other internal quality indices of seeds.