Quantitative predictions of protein and total flavonoids content in Tartary and common buckwheat using near-infrared spectroscopy and chemometrics.

A rapid method was developed for determining the total flavonoid and protein content in Tartary buckwheat by employing near-infrared spectroscopy (NIRS) and various machine learning algorithms, including partial least squares regression (PLSR), support vector regression (SVR), and backpropagation neural network (BPNN). The RAW-SPA-CV-SVR model exhibited superior predictive accuracy for both Tartary and common buckwheat, with a high coefficient of determination (R2p = 0.9811) and a root mean squared error of prediction (RMSEP = 0.1071) for flavonoids, outperforming both PLSR and BPNN models. Additionally, the MMN-SPA-PSO-SVR model demonstrated exceptional performance in predicting protein content (R2p = 0.9247, RMSEP = 0.3906), enhancing the effectiveness of the MMN preprocessing technique for preserving the original data distribution. These findings indicate that the proposed methodology could efficiently assess buckwheat adulteration analysis. It can also provide new insights for the development of a promising method for quantifying food adulteration and controlling food quality.

An integrated and rapid evaluation of Curcumae Radix from different botanical origins based on chemical components, antiplatelet aggregation effect and Fourier transform near-infrared spectroscopy.

Curcumae Radix (CR) is a widely used traditional Chinese medicine with significant pharmaceutical importance, including enhancing blood circulation and addressing blood stasis. This study aims to establish an integrated and rapid quality assessment method for CR from various botanical origins, based on chemical components, antiplatelet aggregation effects, and Fourier transform near-infrared (FT-NIR) spectroscopy combined with multivariate algorithms. Firstly, ultra-performance liquid chromatography-photodiode array (UPLC-PDA) combined with chemometric analyses was used to examine variations in the chemical profiles of CR. Secondly, the activation effect on blood circulation of CR was assessed using an in vitro antiplatelet aggregation assay. The studies revealed significant variations in chemical profiles and antiplatelet aggregation effects among CR samples from different botanical origins, with constituents such as germacrone, ß-elemene, bisdemethoxycurcumin, demethoxycurcumin, and curcumin showing a positive correlation with antiplatelet aggregation biopotency. Thirdly, FT-NIR spectroscopy was integrated with various machine learning algorithms, including Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Subspace K-Nearest Neighbors (Subspace KNN), to classify CR samples from four distinct sources. The result showed that FT-NIR combined with KNN and SVM classification algorithms after SNV and MSC preprocessing successfully distinguished CR samples from four plant sources with an accuracy of 100%. Finally, Quantitative models for active constituents and antiplatelet aggregation bioactivity were developed by optimizing the partial least squares (PLS) model with interval combination optimization (ICO) and competitive adaptive reweighted sampling (CARS) techniques. The CARS-PLS model achieved the best predictive performance across all five components. The coefficient of determination (R2p) and root mean square error (RMSEP) in the independent test sets were 0.9708 and 0.2098, 0.8744 and 0.2065, 0.9511 and 0.0034, 0.9803 and 0.0066, 0.9567 and 0.0172 for germacrone, ß-elemene, bisdemethoxycurcumin, demethoxycurcumin and curcumin, respectively. The ICO-PLS model demonstrated superior predictive capabilities for antiplatelet aggregation biotency, achieving an R2p of 0.9010, and an RMSEP of 0.5370. This study provides a valuable reference for the quality evaluation of CR in a more rapid and comprehensive manner.

Sugarcane disease recognition through visible and near-infrared spectroscopy using deep learning assisted continuous wavelet transform-based spectrogram.

Utilizing visible and near-infrared (Vis-NIR) spectroscopy in conjunction with chemometrics methods has been widespread for identifying plant diseases. However, a key obstacle involves the extraction of relevant spectral characteristics. This study aimed to enhance sugarcane disease recognition by combining convolutional neural network (CNN) with continuous wavelet transform (CWT) spectrograms for spectral features extraction within the Vis-NIR spectra (380-1400 nm) to improve the accuracy of sugarcane diseases recognition. Using 130 sugarcane leaf samples, the obtained one-dimensional CWT coefficients from Vis-NIR spectra were transformed into two-dimensional spectrograms. Employing CNN, spectrogram features were extracted and incorporated into decision tree, K-nearest neighbour, partial least squares discriminant analysis, and random forest (RF) calibration models. The RF model, integrating spectrogram-derived features, demonstrated the best performance with an average precision of 0.9111, sensitivity of 0.9733, specificity of 0.9791, and accuracy of 0.9487. This study may offer a non-destructive, rapid, and accurate means to detect sugarcane diseases, enabling farmers to receive timely and actionable insights on the crops' health, thus minimizing crop loss and optimizing yields.

Cysteine triggered cascade reaction forming coumarin: Visualization of cysteine fluctuation in alcoholic liver disease by a NIR fluorescent probe.

Alcoholic liver disease (ALD) is a chronic toxic liver injury caused by long-term heavy drinking. Due to the increasing incidence, ALD is becoming one of important medical tasks. Many studies have shown that the main mechanism of liver damage caused by large amounts of alcohol may be related to antioxidant stress. As an important antioxidant, cysteine (Cys) is involved in maintaining the normal redox balance and detoxifying metabolic function of the liver, which may be closely related to the pathogenesis of ALD. Therefore, it is necessary to develop a simple non-invasive method for rapid monitoring of Cys in liver. Thus, a near-infrared (NIR) fluorescent probe DCI-Ac-Cys which undergoes Cys triggered cascade reaction to form coumarin fluorophore is developed. Using the DCI-Ac-Cys, decreased Cys was observed in the liver of ALD mice. Importantly, different levels of Cys were monitored in the livers of ALD mice taking silybin and curcumin with the antioxidant effects, indicating the excellent therapeutic effect on ALD. This study provides the important references for the accurate diagnosis of ALD and the pharmacodynamic evaluation of silybin and curcumin in the treatment of ALD, and support new ideas for the pathogenesis of ALD.

Visible-short wavelength near infrared hyperspectral imaging coupled with multivariate curve resolution-alternating least squares for diagnosis of breast cancer.

This study investigates the application of visible-short wavelength near-infrared hyperspectral imaging (Vis-SWNIR HSI) in the wavelength range of 400-950 nm and advanced chemometric techniques for diagnosing breast cancer (BC). The research involved 56 ex-vivo samples encompassing both cancerous and non-cancerous breast tissue from females. First, HSI images were analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) to exploit pure spatial and spectral profiles of active components. Then, the MCR-ALS resolved spatial profiles were arranged in a new data matrix for exploration and discrimination between benign and cancerous tissue samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The PLS-DA classification accuracy of 82.1 % showed the potential of HSI and chemometrics for non-invasive detection of BC. Additionally, the resolved spectral profiles by MCR-ALS can be used to track the changes in the breast tissue during cancer and treatment. It is concluded that the proposed strategy in this work can effectively differentiate between cancerous and non-cancerous breast tissue and pave the way for further studies and potential clinical implementation of this innovative approach, offering a promising avenue for improving early detection and treatment outcomes in BC patients.

Rapid detection of colored and colorless macro- and micro-plastics in complex environment via near-infrared spectroscopy and machine learning.

To better understand the migration behavior of plastic fragments in the environment, development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary. However, most of the studies had focused only on colored plastic fragments, ignoring colorless plastic fragments and the effects of different environmental media (backgrounds), thus underestimating their abundance. To address this issue, the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis (PLS-DA), extreme gradient boost, support vector machine and random forest classifier. The effects of polymer color, type, thickness, and background on the plastic fragments classification were evaluated. PLS-DA presented the best and most stable outcome, with higher robustness and lower misclassification rate. All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm. A two-stage modeling method, which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background, was proposed. The method presented an accuracy higher than 99% in different backgrounds. In summary, this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.

Exploring near-infrared autofluorescence properties in parathyroid tissue: an analysis of fresh and paraffin-embedded thyroidectomy specimens.

Significance: Near-infrared autofluorescence (NIRAF) utilizes the natural autofluorescence of parathyroid glands (PGs) to improve their identification during thyroid surgeries, reducing the risk of inadvertent removal and subsequent complications such as hypoparathyroidism. This study evaluates NIRAF's effectiveness in real-world surgical settings, highlighting its potential to enhance surgical outcomes and patient safety. Aim: We evaluate the effectiveness of NIRAF in detecting PGs during thyroidectomy and central neck dissection and investigate autofluorescence characteristics in both fresh and paraffin-embedded tissues. Approach: We included 101 patients diagnosed with papillary thyroid cancer who underwent surgeries in 2022 and 2023. We assessed NIRAF's ability to locate PGs, confirmed via parathyroid hormone assays, and involved both junior and senior surgeons. We measured the accuracy, speed, and agreement levels of each method and analyzed autofluorescence persistence and variation over 10 years, alongside the expression of calcium-sensing receptor (CaSR) and vitamin D. Results: NIRAF demonstrated a sensitivity of 89.5% and a negative predictive value of 89.1%. However, its specificity and positive predictive value (PPV) were 61.2% and 62.3%, respectively, which are considered lower. The kappa statistic indicated moderate to substantial agreement (kappa = 0.478; P < 0.001 ). Senior surgeons achieved high specificity (86.2%) and PPV (85.3%), with substantial agreement (kappa = 0.847; P < 0.001 ). In contrast, junior surgeons displayed the lowest kappa statistic among the groups, indicating minimal agreement (kappa = 0.381; P < 0.001 ). Common errors in NIRAF included interference from brown fat and eschar. In addition, paraffin-embedded samples retained stable autofluorescence over 10 years, showing no significant correlation with CaSR and vitamin D levels. Conclusions: NIRAF is useful for PG identification in thyroid and neck surgeries, enhancing efficiency and reducing inadvertent PG removals. The stability of autofluorescence in paraffin samples suggests its long-term viability, with false positives providing insights for further improvements in NIRAF technology.

Effects of coffee intake on skeletal muscle microvascular reactivity at rest and oxygen extraction during exercise: a randomized cross-over trial.

PURPOSE: The effects of coffee ingestion on skeletal muscle microvascular function are not well understood. This study aimed to investigate the acute effects of coffee intake with varying levels of caffeine on skeletal muscle microvascular reactivity at rest and oxygen extraction during maximal incremental exercise in physically active individuals. METHODS: Twenty healthy young male participants were administered coffee with low caffeine (3 mg/kg body weight; LC), high caffeine (6 mg/kg body weight; HC), and placebo (decaf) in different sessions. Skeletal muscle reactivity indexes, including tissue saturation index 10s slope (TSI10) and TSI half time recovery (TSI ½) following 5-minute ischemia were measured at rest and were measured at baseline and post-coffee consumption using near-infrared spectroscopy (NIRS). Post-coffee intake, NIRS was also used to measure microvascular oxygen extraction during exercise via maximal incremental exercise. Peak oxygen consumption and peak power output (Wpeak) were simultaneously evaluated. RESULTS: Post-coffee consumption, TSI10 was significantly higher in the LC condition compared to placebo (p = 0.001) and significantly higher in the HC condition compared to placebo (p < 0.001). However, no difference was detected between LC and HC conditions (p = 0.527). HC condition also showed significant less TSI ½ compared to placebo (p = 0.005). However, no difference was detected for microvascular oxygen extraction during exercise, despite the greater Wpeak found for HC condition (p < 0.001) compared to placebo. CONCLUSION: Coffee ingestion with high caffeine level (6 mg/kg body weight) significantly enhanced skeletal muscle reactivity at rest. However, the improvement of exercise performance with coffee intake is not accompanied by alterations in muscle oxygen extraction.

Detection and quantification of groundnut oil adulteration with machine learning using a comparative approach with NIRS and UV-VIS.

Groundnut oil is known as a good source of essential fatty acids which are significant in the physiological development of the human body. It has a distinctive fragrant making it ideal for cooking which contribute to its demand on the market. However, some groundnut oil producers have been suspected to produce groundnut oil by blending it with cheaper oils especially palm olein at different concentrations or by adding groundnut flavor to palm olein. Over the years, there have been several methods to detect adulteration in oils which are time-consuming and expensive. Near infrared (NIR) and ultraviolet-visible (UV-Vis) spectroscopies are cheap and rapid methods for oil adulteration. This present study aimed to apply NIR and UV-Vis in combination with chemometrics to develop models for prediction and quantification of groundnut oil adulteration. Using principal component analysis (PCA) scores, pure and prepared adulterated samples showed overlapping showing similarities between them. Linear discriminant analysis (LDA) models developed from NIR and UV-Vis gave an average cross-validation accuracy of 92.61% and 62.14% respectively for pure groundnut oil and adulterated samples with palm olein at 0, 1, 3, 5, 10, 20, 30, 40 and 50% v/v. With partial least squares regression free fatty acid, color parameters, peroxide and iodine values could be predicted with R2CV's up to 0.8799 and RMSECV's lower than 3 ml/100 ml for NIR spectra and R2CV's up to 0.81 and RMSECV's lower than 4 ml/100 ml for UV-Vis spectra. NIR spectra produced better models as compared to UV-Vis spectra.

A personalized clinical assessment: multi-sensor approach for understanding musculoskeletal health in the frail population.

BACKGROUND: Sarcopenia is a muscle disorder causing a progressive reduction of muscle mass and strength, but the mechanism of its manifestation is still partially unknown. The three main parameters to assess are: muscle strength, muscle volume or quality and low physical performance. There is not a definitive approach to assess the musculoskeletal condition of frail population and often the available tests to be performed in those clinical bedridden patients is reduced because of physical impairments. In this paper, we propose a novel instrumental multi-domain and non-invasive approach during a well-defined protocol of measurements for overcoming these limitations. A group of 28 bedridden elder people, subjected to surgery after hip fracture, was asked to perform voluntary isometric contractions at the 80% of their maximum voluntary contraction with the non-injured leg. The sensor employed before and/or during the exercise were: ultrasound to determine the muscle architecture (vastus lateralis); force acquisition with a load cell placed on the chair, giving an indication of the muscle strength; surface electromyography (EMG) for monitoring muscular electrical activity; time-domain (TD) near-infrared spectroscopy (NIRS) for evaluating muscle oxidative metabolism. RESULTS: A personalized "report card" for each subject was created. It includes: the force diagram (both instantaneous and cumulative, expected and measured); the EMG-force diagram for a comparison between EMG derived median frequency and measured force; two graphs related to the hemodynamic parameters for muscle oxidative metabolism evaluation, i.e., oxy-, deoxy-, total-hemoglobin and tissue oxygen saturation for the whole exercise period. A table with the absolute values of the previous hemodynamic parameters during the rest and the ultrasound related parameters are also included. CONCLUSIONS: In this work, we present the union of protocols, multi-domain sensors and parameters for the evaluation of the musculoskeletal condition. The novelties are the use of sensors of different nature, i.e., force, electrical and optical, together with a new way to visualize and combine the results, by means of a concise, exhaustive and personalized medical report card for each patient. This assessment, totally non-invasive, is focused on a bedridden population, but can be extended to the monitoring of rehabilitation progresses or of the training of athletes.

Emerging Hybrid Intracoronary Imaging Technologies and Their Applications in Clinical Practice and Research.

Intravascular ultrasound and optical coherence tomography are used with increasing frequency for the care of coronary patients and in research studies. These imaging tools can identify culprit lesions in acute coronary syndromes, assess coronary stenosis severity, guide percutaneous coronary intervention (PCI), and detect vulnerable plaques and patients. However, they have significant limitations that have stimulated the development of multimodality intracoronary imaging catheters, which provide improvements in assessing vessel wall pathology and guiding PCI. Prototypes combining 2 or even 3 imaging probes with complementary attributes have been developed, and several multimodality systems have already been used in patients, with near-infrared spectroscopy intravascular ultrasound-based studies showing promising results for the identification of high-risk plaques. Moreover, postmortem histology studies have documented that hybrid imaging catheters can enable more accurate characterization of plaque morphology than standalone imaging. This review describes the evolution in the field of hybrid intracoronary imaging; presents the available multimodality catheters; and discusses their potential role in PCI guidance, vulnerable plaque detection, and the assessment of endovascular devices and emerging pharmacotherapies targeting atherosclerosis.

Eye-tracker and fNIRS: Using neuroscientific tools to assess the learning experience during children's educational robotics activities.

In technology education, there has been a paradigmatic shift towards student-centered approaches such as learning by doing, constructionism, and experiential learning. Educational robotics allows students to experiment with building and interacting with their creations while also fostering collaborative work. However, understanding the student's response to these approaches is crucial to adapting them during the teaching-learning process. In this sense, neuroscientific tools such as Functional Near-Infrared Spectroscopy and Eye-tracker could be useful, allowing the investigation of relevant states experienced by students. Although they have already been used in educational research, their practical relevance in the teaching-learning process has not been extensively investigated. In this perspective article expressing our position, we bring four examples of learning experiences in a robotics class with children, in which we illustrate the usefulness of these tools.

Determination of leaf nitrogen content in apple and jujube by near-infrared spectroscopy.

The nitrogen content of apple leaves and jujube leaves is an important index to judge the growth and development of apple trees and jujube trees to a certain extent. The prediction performance of the two samples was compared between different models for leaf nitrogen content, respectively. The near-infrared absorption spectra of 287 apple leaf samples and 192 jujube leaf samples were collected. After eliminating the outliers by Mahalanobis distance method, the remaining spectral data were processed by six different preprocessing methods. BP neural network (BP), random forest regression (RF), least partial squares (PLS), K-Nearest Neighbor (KNN), and support vector regression (SVR) were compared to establish prediction models of nitrogen content in apple leaves and jujube leaves. The results showed that the determination coefficient (R2), root mean square error (RMSE) and residual prediction deviation (RPD) of the models established by different combined pretreatment methods were compared among the five methods. Compared with the performance of the other four models, the modeling method of SG + SD + CARS + RF was suitable for the prediction of nitrogen content in apple leaves, and its modeling set R2 was 0.85408, RMSE was 0.082188, and RPD was 2.5864. The validation set R2 is 0.75527, RMSE is 0.099028, RPD is 2.1956. The modeling method of FD + CARS + PLS was suitable for the prediction of nitrogen content in jujube leaves. The modeling set R2 was 0.7954, RMSE was 0.14558, and RPD was 2.4264; the validation set R2 is 0.81348, RMSE is 0.089217, and RPD is 2.4552.In the prediction modeling of apple leaf nitrogen content in the characteristic band, the model quality of RF was better than the other four prediction models. The model quality of PLS in predictive modeling of nitrogen content of jujube leaves in characteristic bands is superior to the other four predictive models, These results provide a reference for the use of near-infrared spectroscopy to determine whether apple trees and jujube trees are deficient in nutrients.

[Prediction model of Reduning Injection content based on near-infrared and mid-infrared technology combined with spectral fusion].

In this paper, a method for rapidly determining the content of chlorogenic acid, neochlorogenic acid, cryptochlorogenic acid, gardeniside, and strychnoside in Reduning Injection(RI) was established based on near-infrared spectroscopy(NIRS), midinfrared spectroscopy(MIRS), and spectral fusion technology. Six pretreatment methods and five variable screening methods were investigated, and the best method was selected to establish a partial least square(PLS) model of two single spectra. At the same time,the NIRS and MIRS were fused with equal weights and characteristic bands, and the PLS model was established. The prediction effect of the four models on the quality control components was compared: NIRS>characteristic band fusion>MIRS>equal weight fusion. The relative standard error of prediction(RSEP) of the NIRS models on the five quality control components was less than 2. 5%, and the ratio of performance to deviation(RPD) was greater than 9. 5. The results show that the single spectrum model of NIRS is the best quantitative detection method, and the model of NIRS combined with the PLS algorithm can be used for the rapid detection of Reduning Injection.

Language networks of normal-hearing infants exhibit topological differences between resting and steady states: An fNIRS functional connectivity study.

Task-related studies have consistently reported that listening to speech sounds activate the temporal and prefrontal regions of the brain. However, it is not well understood how functional organization of auditory and language networks differ when processing speech sounds from its resting state form. The knowledge of language network organization in typically developing infants could serve as an important biomarker to understand network-level disruptions expected in infants with hearing impairment. We hypothesized that topological differences of language networks can be characterized using functional connectivity measures in two experimental conditions (1) complete silence (resting) and (2) in response to repetitive continuous speech sounds (steady). Thirty normal-hearing infants (14 males and 16 females, age: 7.8 ± 4.8 months) were recruited in this study. Brain activity was recorded from bilateral temporal and prefrontal regions associated with speech and language processing for two experimental conditions: resting and steady states. Topological differences of functional language networks were characterized using graph theoretical analysis. The normalized global efficiency and clustering coefficient were used as measures of functional integration and segregation, respectively. We found that overall, language networks of infants demonstrate the economic small-world organization in both resting and steady states. Moreover, language networks exhibited significantly higher functional integration and significantly lower functional segregation in resting state compared to steady state. A secondary analysis that investigated developmental effects of infants aged 6-months or below and above 6-months revealed that such topological differences in functional integration and segregation across resting and steady states can be reliably detected after the first 6-months of life. The higher functional integration observed in resting state suggests that language networks of infants facilitate more efficient parallel information processing across distributed language regions in the absence of speech stimuli. Moreover, higher functional segregation in steady state indicates that the speech information processing occurs within densely interconnected specialized regions in the language network.

Research on brain functional network property analysis and recognition methods targeting brain fatigue.

At present, researches on brain fatigue recognition are still in the stage of single task and simple brain region network features, while researches on high-order brain functional network features and brain region state mechanisms during fatigue in multi-task scenarios are still insufficient, making it difficult to meet the needs of fatigue recognition under complex conditions. Therefore, this study utilized functional near-infrared spectroscopy (fNIRS) technology to explore the correlation and differences in the low-order and high-order brain functional network attributes of three task induced mental fatigue, and to explore the brain regions that have a major impact on mental fatigue. Self-training algorithms were used to identify the three levels of brain fatigue. The results showed that during the fatigue development, the overall connection strength of the endothelial cell metabolic activity and neural activity frequency bands of the low-order brain functional network first decreased and then increased, while the myogenic activity and heart rate activity frequency bands showed the opposite pattern. Network topology analysis indicated that from no fatigue to mild fatigue, the clustering coefficient of endothelial cell metabolic activity and myogenic activity frequency bands significantly decreased, while the characteristic path length of myogenic activity significantly increased; when experiencing severe fatigue, the small-world attribute of the neural frequency band significantly weakened. However, each frequency band maintained its small-world attribute, reflecting the self-optimization and adaptability of the network during the fatigue process. During mild fatigue, neuronal activity bands' node degree, cluster coefficient, and efficiency rose in high-order brain networks, while low-order networks showed no significant changes. As fatigue progressed, the myogenic activity bands of high-order network properties dominated, but neural bands gained prominence in mild fatigue, approaching the level of myogenic bands in severe fatigue, indicating that brain fatigue orchestrated a shift from myogenic to neural dominance in frequency bands. In addition, during the process of fatigue, the four network attributes of the high-order network cluster composed of low-order nodes related to the prefrontal cortex region, left anterior motor region, motor assist region, and left frontal lobe eye movement region significantly increased, indicating that these brain regions had a significant impact on brain fatigue status. The accuracy of using both high-order and low-order features to identify fatigue levels reached 88.095%, indicating that the combined network features of both high-order and low-order fNIRS signals could effectively detect multi-level mental fatigue, providing innovative ideas for fatigue warning.

Cortical activation during the verbal fluency task for obstructive sleep apnea patients with depressive symptoms: A multi-channel fNIRS study.

STUDY OBJECTIVE: The aim of our study was to elucidate differences in brain activity patterns among obstructive sleep apnea (OSA) patients, OSA patients with depressive symptoms, and healthy controls (HCs). We also investigated the relationship between brain function and depression in OSA patients. METHODS: A total of 95 subjects were included in the study, including 34 OSA patients without depressive symptoms, 31 OSA patients with depressive symptoms, and 30 HCs. The 53-channel functional near-infrared spectroscopy (fNIRS) was used to monitor the concentration of oxy-hemoglobin (Oxy-Hb) in the brain, whereas the participants performed the verbal fluency task, and the degree of depression was scored using the 17-item Hamilton Rating Scale for Depression (HAMD-17). Hierarchical regression models were conducted to analyze the association of fNIRS features with depressive symptom. RESULTS: The Oxy-Hb changes of the three groups were significantly different in Channels 25 (H = 9.878, p = .007) and 43 (H = 6.957, p = .031). Inter-group comparisons showed that the Oxy-Hb change of Channel 25 (located in the dorsolateral prefrontal cortex [DLPFC]) in OSA group was less than that in HC group (p = .006), and the Oxy-Hb change of Channel 43 (located in the right frontal polar region) in OSA group with depression was less than that in OSA group (p = .025). Spearman's test showed that there was a significant negative correlation between HAMD-17 scores and mean Oxy-Hb changes in Channel 43 (r = -.319, p < .05) in the OSA patients. Using hierarchical regression, Oxy-Hb changes in Channel 43 accounted for a significant proportion of the variation in outcome variables, even when accounting for other polysomnography features. CONCLUSIONS: Changes in the hemodynamic response of DLPFC may be a potential mechanism of executive dysfunction in OSA patients. And the right frontal polar region may be significant in assessing depressive symptoms in patients with OSA.

[Study on the impact of aging on the brain activity patterns in auditory speech comprehension dual-route regions].

Objective: To elucidate the patterns of neural activity alterations associated with auditory speech comprehension across the lifespan and the impact of varying listening environments on these dynamics. Methods: Functional near-infrared spectroscopy (fNIRS) was employed to measure the concentration of oxygenated hemoglobin in the brains of 93 adults aged from 20 to 70 with normal hearing. These participants were recruited from Beijing Tongren Hospital, affiliated with Capital Medical University, between March 2021 and February 2023. Brain activity was recorded as subjects passively listened to sentences in both silent and noise conditions with varying signal-to-noise ratios (SNR). The alterations in brain activity were analyzed to delineate the age-related trends under different auditory conditions. Statistical analysis was performed using SPSS 22.0 software. Results: The bilateral primary auditory cortex, superior temporal gyrus, and Wernicke's area, critical for sound signal discrimination and perception, exhibited enhanced activity post-stimulus presentation. Broca's area, pivotal for speech production, demonstrated an initial decrease in activity followed by an increment after stimulus onset. The ventral middle temporal gyrus and dorsal postcentral gyrus showed augmented activity in later time windows. Furthermore, it was observed that in quiet conditions and at low noise levels (SNR=10 dB), auditory cortical activity diminished with age. With increasing noise levels (SNR=5 dB), compensatory brain regions (right ventral middle temporal gyrus and dorsal postcentral gyrus) showed enhanced activity with advancing age. As noise intensity further escalated (SNR=0, SNR=-5 dB), not only did auditory cortical activity decline, but also the activity in regions associated with semantic processing and motor functions reduced with age. Conclusion: During auditory speech comprehension, dual-pathway brain regions exhibit distinct activity patterns. With heightened noise exposure, an increasing number of brain regions are influenced by aging, manifesting as a general decline in activity in most dual-pathway regions, alongside a selective augmentation in some compensatory regions on the right hemisphere.

Dynamic multispectral NIR/SWIR for in vivo lymphovascular architectural and functional quantification.

Significance: Although the lymphatic system is the second largest circulatory system in the body, there are limited techniques available for characterizing lymphatic vessel function. We report shortwave-infrared (SWIR) imaging for minimally invasive in vivo quantification of lymphatic circulation with superior contrast and resolution compared with near-infrared first window imaging. Aim: We aim to study the lymphatic structure and function in vivo via SWIR fluorescence imaging. Approach: We evaluated subsurface lymphatic circulation in healthy, adult immunocompromised salt-sensitive Sprague-Dawley rats using two fluorescence imaging modalities: near-infrared first window (NIR-I, 700 to 900 nm) and SWIR (900 to 1800 nm) imaging. We also compared two fluorescent imaging probes: indocyanine green (ICG) and silver sulfide quantum dots (QDs) as SWIR lymphatic contrast agents following intradermal footpad delivery in these rats. Results: SWIR imaging exhibits reduced scattering and autofluorescence background relative to NIR-I imaging. SWIR imaging with ICG provides 1.7 times better resolution and sensitivity than NIR-I, and SWIR imaging with QDs provides nearly two times better resolution and sensitivity with enhanced vessel distinguishability. SWIR images thus provide a more accurate estimation of in vivo vessel size than conventional NIR-I images. Conclusions: SWIR imaging of silver sulfide QDs into the intradermal footpad injection provides superior image resolution compared with conventional imaging techniques using NIR-I imaging with ICG dye.

Feasibility of Vis-NIR spectroscopy approach to predict soil biological attributes in arid land soils.

Visible and near-infrared (Vis-NIR) reflectance spectroscopy has recently emerged as an efficient and cost-effective tool for monitoring soil parameters and provides an extensive array of measurements swiftly. This study sought to predict fundamental biological attributes of calcareous soils using spectral reflectance data in the Vis-NIR range through the application of partial least square regression (PLSR) and stepwise multiple linear regression (SMLR) techniques. The objective was to derive spectrotransfer functions (STFs) to predict selected soil biological attributes. A total of 97 composite samples were collected from three distinct agricultural land uses, i.e., sugarcane, wheat, and date palm, in the Khuzestan Province, Iran. The samples were analyzed using both standard laboratory analysis and proximal sensing approach within the Vis-NIR range (400-2500 nm). Biological status was evaluated by determining soil enzyme activities linked to nutrient cycling including acid phosphatase (ACP), alkaline phosphatase (ALP), dehydrogenase (DEH), soil microbial respiration (SMR), microbial biomass phosphorus (Pmic), and microbial biomass carbon (Cmic). The results indicated that the developed PLSR models exhibited superior predictive performance in most biological parameters compared to the STFs, although the differences were not significant. Specifically, the STFs acceptably accurately predicted ACP, ALP, DEH, SMR, Pmic, and Cmic with R2val (val = validation dataset) values of 0.68, 0.67, 0.65, 0.65, 0.76, and 0.72, respectively. These findings confirm the potential of Vis-NIR spectroscopy and the effectiveness of the associated STFs as a rapid and reliable technique for assessing biological soil quality. Overall, in the context of predicting soil properties using spectroscopy-based approaches, emphasis must be placed on developing straightforward, easily deployable, and pragmatic STFs.