RESUMEN
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.
Asunto(s)
Cumarinas , Cisteína , Colorantes Fluorescentes , Hepatopatías Alcohólicas , Animales , Cisteína/análisis , Cisteína/metabolismo , Cumarinas/química , Colorantes Fluorescentes/química , Hepatopatías Alcohólicas/metabolismo , Hepatopatías Alcohólicas/patología , Masculino , Hígado/metabolismo , Hígado/efectos de los fármacos , Hígado/patología , Ratones , Ratones Endogámicos C57BL , Espectroscopía Infrarroja Corta/métodos , Curcumina/farmacología , Espectrometría de Fluorescencia , Silibina/farmacología , Silibina/químicaRESUMEN
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.
Asunto(s)
Neoplasias de la Mama , Imágenes Hiperespectrales , Análisis de Componente Principal , Espectroscopía Infrarroja Corta , Humanos , Femenino , Neoplasias de la Mama/diagnóstico , Análisis de los Mínimos Cuadrados , Espectroscopía Infrarroja Corta/métodos , Imágenes Hiperespectrales/métodos , Análisis Multivariante , Análisis DiscriminanteRESUMEN
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.
Asunto(s)
Curcuma , Inhibidores de Agregación Plaquetaria , Agregación Plaquetaria , Espectroscopía Infrarroja Corta , Curcuma/química , Espectroscopía Infrarroja Corta/métodos , Agregación Plaquetaria/efectos de los fármacos , Espectroscopía Infrarroja por Transformada de Fourier/métodos , Inhibidores de Agregación Plaquetaria/análisis , Inhibidores de Agregación Plaquetaria/química , Animales , Cromatografía Líquida de Alta Presión/métodos , Medicamentos Herbarios Chinos/química , Medicamentos Herbarios Chinos/análisis , Algoritmos , Extractos Vegetales/químicaRESUMEN
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.
Asunto(s)
Aprendizaje Profundo , Enfermedades de las Plantas , Saccharum , Espectroscopía Infrarroja Corta , Análisis de Ondículas , Saccharum/química , Espectroscopía Infrarroja Corta/métodos , Hojas de la Planta/química , Análisis de los Mínimos Cuadrados , Análisis DiscriminanteRESUMEN
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.
Asunto(s)
Fagopyrum , Flavonoides , Proteínas de Plantas , Espectroscopía Infrarroja Corta , Fagopyrum/química , Espectroscopía Infrarroja Corta/métodos , Flavonoides/análisis , Flavonoides/química , Proteínas de Plantas/análisis , Proteínas de Plantas/química , Quimiometría/métodos , Análisis de los Mínimos Cuadrados , Redes Neurales de la ComputaciónRESUMEN
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.
Asunto(s)
Imagen Óptica , Glándulas Paratiroides , Espectroscopía Infrarroja Corta , Tiroidectomía , Humanos , Glándulas Paratiroides/cirugía , Glándulas Paratiroides/metabolismo , Masculino , Femenino , Persona de Mediana Edad , Imagen Óptica/métodos , Adulto , Espectroscopía Infrarroja Corta/métodos , Adhesión en Parafina/métodos , Anciano , Cáncer Papilar Tiroideo/cirugía , Cáncer Papilar Tiroideo/patología , Cáncer Papilar Tiroideo/metabolismo , Receptores Sensibles al Calcio/metabolismo , Receptores Sensibles al Calcio/análisisRESUMEN
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.
Asunto(s)
Monitoreo del Ambiente , Aprendizaje Automático , Plásticos , Espectroscopía Infrarroja Corta , Espectroscopía Infrarroja Corta/métodos , Monitoreo del Ambiente/métodos , Plásticos/análisis , Análisis de los Mínimos Cuadrados , Análisis Discriminante , ColorRESUMEN
The integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can facilitate the advancement of brain-computer interfaces (BCIs). However, existing research in this domain has grappled with the challenge of the efficient selection of features, resulting in the underutilization of the temporal richness of EEG and the spatial specificity of fNIRS data.To effectively address this challenge, this study proposed a deep learning architecture called the multimodal DenseNet fusion (MDNF) model that was trained on two-dimensional (2D) EEG data images, leveraging advanced feature extraction techniques. The model transformed EEG data into 2D images using a short-time Fourier transform, applied transfer learning to extract discriminative features, and consequently integrated them with fNIRS-derived spectral entropy features. This approach aimed to bridge existing gaps in EEG-fNIRS-based BCI research by enhancing classification accuracy and versatility across various cognitive and motor imagery tasks.Experimental results on two public datasets demonstrated the superiority of our model over existing state-of-the-art methods.Thus, the high accuracy and precise feature utilization of the MDNF model demonstrates the potential in clinical applications for neurodiagnostics and rehabilitation, thereby paving the method for patient-specific therapeutic strategies.
Asunto(s)
Interfaces Cerebro-Computador , Aprendizaje Profundo , Electroencefalografía , Espectroscopía Infrarroja Corta , Humanos , Electroencefalografía/métodos , Espectroscopía Infrarroja Corta/métodos , Procesamiento de Señales Asistido por Computador , Encéfalo/fisiología , Encéfalo/diagnóstico por imagen , Adulto , Masculino , FemeninoRESUMEN
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.
Asunto(s)
Malus , Nitrógeno , Hojas de la Planta , Espectroscopía Infrarroja Corta , Ziziphus , Malus/metabolismo , Malus/química , Hojas de la Planta/metabolismo , Hojas de la Planta/química , Ziziphus/metabolismo , Ziziphus/química , Nitrógeno/metabolismo , Nitrógeno/análisis , Espectroscopía Infrarroja Corta/métodos , Análisis de los Mínimos Cuadrados , Redes Neurales de la ComputaciónRESUMEN
In the field of brain-computer interfaces (BCIs) based on functional near-infrared spectroscopy (fNIRS), traditional subject-specific decoding methods suffer from the limitations of long calibration time and low cross-subject generalizability, which restricts the promotion and application of BCI systems in daily life and clinic. To address the above dilemma, this study proposes a novel deep transfer learning approach that combines the revised inception-residual network (rIRN) model and the model-based transfer learning (TL) strategy, referred to as TL-rIRN. This study performed cross-subject recognition experiments on mental arithmetic (MA) and mental singing (MS) tasks to validate the effectiveness and superiority of the TL-rIRN approach. The results show that the TL-rIRN significantly shortens the calibration time, reduces the training time of the target model and the consumption of computational resources, and dramatically enhances the cross-subject decoding performance compared to subject-specific decoding methods and other deep transfer learning methods. To sum up, this study provides a basis for the selection of cross-subject, cross-task, and real-time decoding algorithms for fNIRS-BCI systems, which has potential applications in constructing a convenient and universal BCI system.
Asunto(s)
Interfaces Cerebro-Computador , Espectroscopía Infrarroja Corta , Espectroscopía Infrarroja Corta/métodos , Humanos , Aprendizaje Profundo , Algoritmos , Encéfalo/fisiología , Encéfalo/diagnóstico por imagen , Redes Neurales de la ComputaciónRESUMEN
INTRODUCTION: To date, radical surgery remains the best curative option in patients with early-stage lung cancer. In patients with small lung lesions, video-assisted thoracic surgery (VATS) should be increasingly chosen as a fundamental alternative to thoracotomy as it is associated with less postoperative pain and better quality of life. This scenario necessarily increases the need for thoracic surgeons to implement new localization techniques. The conventional near-infrared (NIR) indocyanine green (ICG) method demonstrated a significant limitation in deep cancer recognition, principally due to its intrinsic low-depth tissue penetration. Similarly, the lymph-node sentinel approach conducted by the ICG method was demonstrated to be inefficient, mainly due to the non-specificity of the tracker and the irregular pathway of pulmonary lymph node drainage. Our study aims to evaluate the effectiveness of Cetuximab- IRDye800CW in marking lung nodules and mediastinal lymph nodes. METHODS AND ANALYSIS: This study is defined as an open-label, single-arm, single-stage phase II trial evaluating the effectiveness of Cetuximab-IRDye800CW in detecting tumors and lymph-node metastases in patients with lung cancer who are undergoing video-assisted thoracic surgery (VATS). Cetuximab is a monoclonal antibody that binds, inhibits, and degrade the EGFR. The IRDye® 800CW, an indocyanine-type NIR fluorophore, demonstrated enhanced tissue penetration compared to other NIR dyes. The combination with the clinical approved monoclonal antibody anti-epidermal growth factor EGFR Cetuximab (Cetuximab-IRDye800) has shown promising results as a specific tracker in different cancer types (i.e., brain, pancreas, head, and neck). The study's primary outcome is focused on the proportion of patients with lung nodules detected during surgery using an NIR camera. The secondary outcomes include a broad spectrum of items, including the proportion of patients with detection of unexpected cancer localization during surgery by NIR camera and the proportion of patients with negative surgical margins, the evaluation of the time spawns between the insertion of the NIR camera and the visualization of the nodule and the possible morbidity of the drug assessed during and after the drug infusion. ETHICS AND DISSEMINATION: This trial has been approved by the Ethical Committee of Azienda Ospedaliera Universitaria Città della Salute e della Scienza di Torino (Torino, Italy) and by the Italian Medicines Agency (AIFA). Findings will be written as methodology papers for conference presentations and published in peer-reviewed journals. The Azienda Ospedaliera Universitaria Città della Salute e della Scienza di Torino, the University of Torino, and the AIRC Public Engagement Divisions will help identify how best to publicize the findings.Trial registration EudraCT 202,100,645,430. CLINICALTRIALS: gov NCT06101394 (October 23, 2023).
Asunto(s)
Neoplasias Pulmonares , Imagen Molecular , Cirugía Torácica Asistida por Video , Humanos , Neoplasias Pulmonares/cirugía , Neoplasias Pulmonares/diagnóstico por imagen , Cirugía Torácica Asistida por Video/métodos , Imagen Molecular/métodos , Espectroscopía Infrarroja Corta/métodos , Cetuximab/uso terapéutico , Cetuximab/administración & dosificación , Verde de Indocianina/administración & dosificación , Metástasis Linfática , Femenino , Masculino , Procedimientos Quirúrgicos Mínimamente Invasivos/métodos , Ganglios Linfáticos/patología , Ganglios Linfáticos/diagnóstico por imagen , Ganglios Linfáticos/cirugíaRESUMEN
Significance: Near-infrared optical imaging methods have shown promise for monitoring response to neoadjuvant chemotherapy (NAC) for breast cancer, with endogenous contrast coming from oxy- and deoxyhemoglobin. Spatial frequency domain imaging (SFDI) could be used to detect this contrast in a low-cost and portable format, but it has limited imaging depth. It is possible that local tissue compression could be used to reduce the effective tumor depth. Aim: To evaluate the potential of SFDI for therapy response prediction, we aim to predict how changes to tumor size, stiffness, and hemoglobin concentration would be reflected in contrast measured by SFDI under tissue compression. Approach: Finite element analysis of compression on an inclusion-containing soft material is combined with Monte Carlo simulation to predict the measured optical contrast. Results: When the effect of compression on blood volume is not considered, contrast gain from compression increases with the size and stiffness of the inclusion and decreases with the inclusion depth. With a model of reduction of blood volume from compression, compression reduces imaging contrast, an effect that is greater for larger inclusions and stiffer inclusions at shallower depths. Conclusions: This computational modeling study represents a first step toward tracking tumor changes induced by NAC using SFDI and local compression.
Asunto(s)
Neoplasias de la Mama , Método de Montecarlo , Neoplasias de la Mama/diagnóstico por imagen , Humanos , Femenino , Simulación por Computador , Espectroscopía Infrarroja Corta/métodos , Análisis de Elementos Finitos , Imagen Óptica/métodos , Fantasmas de Imagen , Modelos Biológicos , Hemoglobinas/análisisRESUMEN
BACKGROUND: Children with developmental coordination disorder (DCD) have impaired online motor control. Researchers posit that this impairment could be due to a deficit in utilizing the internal model control process. However, there is little neurological evidence to support this view because few neuroimaging studies have focused specifically on tasks involving online motor control. Therefore, the aim of this study was to investigate the differences in cortical hemodynamic activity during an online movement adjustment task between children with and without DCD. METHODS: Twenty children with DCD (mean age: 9.88 ± 1.67 years; gender: 14M/6F) and twenty age-and-gender matched children with typical development (TD) (mean age: 9.87 ± 1.59 years; gender: 14M/6F) were recruited via convenience sampling. Participants performed a double-step reaching task under two conditions (with and without online adjustment of reaching). Cortical hemodynamic activity during task in ten regions of interest, including bilateral primary somatosensory cortex, primary motor cortex, premotor cortex, superior parietal cortex, and inferior parietal cortex was recorded using functional near-infrared spectroscopy. In the analyses, change in oxyhemoglobin (ΔHbO) concentration was used to characterize hemodynamic response. Two-way analyses of variance were conducted for each region of interest to compare hemodynamic responses between groups and conditions. Additionally, Pearson's r correlations between hemodynamic response and task performance were performed. RESULTS: Outcome showed that children with DCD required significantly more time to correct their reaching movements compared to the control group (t = 3.948, P < 0.001). Furthermore, children with DCD have a significantly lower ΔHbO change in the left superior parietal cortex during movement correction, compared to children with TD (F = 4.482, P = 0.041). Additionally, a significant negative correlation (r = - 0.598, P < 0.001) was observed between the difference in movement time of reaching and the difference in ΔHbO between conditions in the left superior parietal cortex. CONCLUSIONS: The findings of this study suggest that deficiencies in processing real-time sensory feedback, considering the function of the superior parietal cortex, might be related to the impaired online motor control observed in children with DCD. Interventions could target this issue to enhance their performance in online motor control.
Asunto(s)
Trastornos de la Destreza Motora , Espectroscopía Infrarroja Corta , Humanos , Masculino , Femenino , Espectroscopía Infrarroja Corta/métodos , Niño , Trastornos de la Destreza Motora/fisiopatología , Trastornos de la Destreza Motora/diagnóstico por imagen , Estudios Transversales , Desempeño Psicomotor/fisiología , Corteza Cerebral/diagnóstico por imagen , Corteza Cerebral/fisiopatología , Hemodinámica/fisiologíaRESUMEN
Early childhood marks a pivotal period in the maturation of executive function, the cognitive ability to consciously regulate actions and thoughts. Mindfulness-based interventions have shown promise in bolstering executive function in children. This study used the functional near-infrared spectroscopy technique to explore the impact of mindfulness-based training on young children. Brain imaging data were collected from 68 children (41 boys, aged 61.8 ± 10.7 months) who were randomly assigned to either an intervention group (N = 37, aged 60.03 ± 11.14 months) or a control group (N = 31, aged 59.99 ± 10.89 months). Multivariate and multiscale sample entropy analyses were used. The results showed that: (1) brain complexity was reduced in the intervention group after receiving the mindfulness-based intervention in all three executive function tasks (ps < 0.05), indicating a more efficient neural processing mechanism after the intervention; (2) difference comparisons between the intervention and control groups showed significant differences in relevant brain regions during cognitive shifting (left dorsolateral prefrontal cortex and medial prefrontal cortex) and working memory tasks (left dorsolateral prefrontal cortex), which corroborates with improved behavioral results in the intervention group (Z = -3.674, P < 0.001 for cognitive shifting; Z = 2.594, P < 0.01 for working memory). These findings improve our understanding of early brain development in young children and highlight the neural mechanisms by which mindfulness-based interventions affect executive function. Implications for early intervention to promote young children's brain development are also addressed.
Asunto(s)
Encéfalo , Función Ejecutiva , Atención Plena , Espectroscopía Infrarroja Corta , Humanos , Atención Plena/métodos , Masculino , Femenino , Función Ejecutiva/fisiología , Preescolar , Encéfalo/fisiología , Encéfalo/diagnóstico por imagen , Entropía , Memoria a Corto Plazo/fisiología , Análisis Multivariante , Pruebas NeuropsicológicasRESUMEN
A predictive model utilizing near-infrared spectroscopy was developed to estimate the loss on drying, total contents of crocin I and crocin II, and picrocrocin content of saffron. Initially, the LD values were determined using a moisture-ash analyzer, while HPLC was employed for measuring the total contents of crocin I, crocin II, and picrocrocin. The near-infrared spectra of 928 saffron samples were collected and preprocessed using first derivative, standard normal variable transformation, detrended correction, multivariate scattering correction, Savitzky-Golay smoothing, and mean centering methods. Leveraging the partial least squares method, regression models were constructed, with parameters optimized through a selective combination of the above six preprocessing methods. Subsequently, prediction models for loss on drying, total contents of crocin I and crocin II, and picrocrocin content were established, and the prediction accuracy of the models was verified. The correlation coefficients and root mean square error of loss on drying, total contents of crocin I and crocin II, and picrocrocin content demonstrated high accuracy, with R2 values of 0.8627, 0.8851, and 0.8592 and root mean square error values of 0.0260, 0.0682, and 0.0465. This near-infrared prediction model established in the present study offers a precise and efficient means of assessing loss on drying, total contents of crocin I and crocin II, and picrocrocin content in saffron and is useful for the development of a rapid quality evaluation system.
Asunto(s)
Carotenoides , Crocus , Espectroscopía Infrarroja Corta , Crocus/química , Espectroscopía Infrarroja Corta/métodos , Carotenoides/análisis , Análisis de los Mínimos Cuadrados , Cromatografía Líquida de Alta Presión/métodos , Glucósidos , Terpenos , CiclohexenosRESUMEN
Milk powders are becoming a major attraction for many industrial applications due to their nutritional and functional properties. Different types of powdered milk, each with their own distinct chemical compositions, can have different functionalities. Consequently, the development of rapid monitoring methods is becoming an urgent task to explore and expand their applicability. Lately, there is growing emphasis on the potential of near-infrared spectroscopy (NIRS) as a rapid technique for the quality assessment of dairy products. In the present work, we explored the potential of NIRS coupled with chemometrics for the prediction of the main functional and chemical properties of three types of milk powders, as well as their important processing parameters. Mare, camel and cow milk powders were prepared at different concentrations (5%, 10% and 12%) and temperatures (25 °C, 40 °C and 65 °C), and then their main physicochemical attributes and NIRS spectra were analyzed. Overall, high accuracy in both recognition and prediction based on type, concentration and temperature was achieved by NIRS-based models, and the quantification of quality attributes (pH, viscosity, dry matter content, fat content, conductivity and individual amino acid content) also resulted in high accuracy in the models. R2CV and R2pr values ranging from 0.8 to 0.99 and 0.7 to 0.98, respectively, were obtained by using PLSR models. However, SVR models achieved higher R2CV and R2pr values, ranging from 0.91 to 0.99 and 0.80 to 0.99, respectively.
Asunto(s)
Camelus , Leche , Polvos , Espectroscopía Infrarroja Corta , Animales , Espectroscopía Infrarroja Corta/métodos , Leche/química , Polvos/química , Bovinos , Caballos , Quimiometría/métodos , FemeninoRESUMEN
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.
Asunto(s)
Conectoma , Red Nerviosa , Espectroscopía Infrarroja Corta , Percepción del Habla , Humanos , Femenino , Masculino , Lactante , Red Nerviosa/diagnóstico por imagen , Red Nerviosa/fisiología , Percepción del Habla/fisiología , Conectoma/métodos , Corteza Prefrontal/fisiología , Corteza Prefrontal/diagnóstico por imagen , LenguajeRESUMEN
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.
Asunto(s)
Enfermedad de la Arteria Coronaria , Vasos Coronarios , Imagen Multimodal , Intervención Coronaria Percutánea , Placa Aterosclerótica , Valor Predictivo de las Pruebas , Tomografía de Coherencia Óptica , Ultrasonografía Intervencional , Humanos , Enfermedad de la Arteria Coronaria/diagnóstico por imagen , Enfermedad de la Arteria Coronaria/terapia , Vasos Coronarios/diagnóstico por imagen , Vasos Coronarios/patología , Intervención Coronaria Percutánea/instrumentación , Diseño de Equipo , Catéteres Cardíacos , Difusión de Innovaciones , Cateterismo Cardíaco/instrumentación , Espectroscopía Infrarroja Corta , AnimalesRESUMEN
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.
Asunto(s)
Contaminación de Alimentos , Aprendizaje Automático , Espectrofotometría Ultravioleta , Espectroscopía Infrarroja Corta , Espectroscopía Infrarroja Corta/métodos , Contaminación de Alimentos/análisis , Espectrofotometría Ultravioleta/métodos , Análisis de Componente Principal , Análisis Discriminante , Aceite de Cacahuete/análisis , Aceite de Palma/químicaRESUMEN
While it is widely acknowledged that exercise has positive effects on cognitive function, the specific impacts of different types of exercises, particularly open and closed skill exercises, on cognitive impairment continue to be a debated topic. In this study, we used fNIRS and cognitive psychology tasks to investigate the effects of different types of exercises on cognitive function and brain activity in young adults. We conducted an observational study to assess the cognitive function of participants who had engaged in these exercises for a long period. Additionally, we examined the effects of open skill exercise (badminton) and closed skill exercise (calisthenics) on localized blood flow in the prefrontal lobe of the brain using an experimental research method. Specifically, during the Stroop task, the badminton group exhibited significantly higher â³HbO2 in channel 18, corresponding to the dorsolateral prefrontal cortex, compared to the calisthenics group (F = 4.485, P < 0.05, η2 = 0.074). In the 2-back task, the calisthenics group showed significantly higher â³HbO2 in channel 17, corresponding to the frontopolar area, dorsolateral prefrontal cortex and inferior prefrontal gyrus, than the badminton group (F = 8.842, P < 0.01, η2 = 0.136). Our findings reveal that open skill exercises are more effective in enhancing cognitive inhibition, thereby increasing attention capacity, self-regulation, and flexibility in response to environmental changes. Conversely, closed skill exercises demonstrate greater efficacy in improving working memory within cognitive functions, showcasing an enhanced capacity for information processing and storage. These data indicate that while both open and closed skill exercises are beneficial for cognitive function, they exhibit significant distinctions in some aspects.