Abstract: The application of Non-Invasive Respiratory Support (NIRS) has been increasingly used outside the pediatric intensive care units (PICUs). Early identification of disease progression or poor response to therapy is crucial for timely referral. Objective: To describe the use of NIRS in general pediatric wards and to evaluate the risk factors associated with therapy failure and PICU referral. Patients and Method: Retrospective, observational, single-center study in children under 24 months with acute lower respiratory tract infection (LRTI) who received NIRS in general wards during the winter seasons of 2021-2022. NIRS included high-flow nasal cannula (HFNC), continuous positive airway pressure (CPAP), or bilevel positive airway pressure (BiPAP). Each event was individually characterized, documenting the type of support and its clinical response (success or failure). Failure was defined as the need for PICU admission. Demographic factors, clinical characteristics, and types of support used were compared between these groups using bivariate analysis and multivariate logistic regression. Results: Out of a total of 415 patients, 14.2% experienced therapy failure. The main predictors of failure were age (adjusted OR = 0.87; 95% CI: 0.81-0.94), prematurity (adjusted OR = 2.52; 95% CI: 1.07-5.96), the presence of congenital heart disease (adjusted OR = 5.92; 95% CI: 2.1316.42), and a higher heart rate (adjusted OR = 1.25; 95% CI: 1.06-1.48). Clinical outcomes did not vary according to the type of NIRS used. Regarding the type of NIRS received, HFNC was used in 68.6% of the events, followed by CPAP (25.3%) and BiPAP (6.1%). Conclusions: The use of NIRS in general pediatric wards prevented PICU referral in 85.8% of cases. The identified risk factors for referral included younger age, prematurity, the presence of congenital heart disease, and a higher initial heart rate. HFNC emerged as the most commonly used respiratory support technique, followed by CPAP and BiPAP, which were primarily used as rescue strategies.

Resumen: La aplicación de Soporte Respiratorio No Invasivo (SRNI) ha experimentado una creciente aplicación fuera de unidades de cuidados intensivos pediátricos (UCIP), siendo relevante la identificación temprana de progresión de enfermedad o pobre respuesta a la terapia suministrada para una derivación oportuna. Objetivo: Describir el uso de SRNI en salas generales de pediatría y evaluar los factores de riesgo relacionados a fracaso de terapia y derivación a UCIP. Pacientes y Método: estudio observacional, retrospectivo, monocéntrico, en niños menores de 24 meses con infección respiratoria aguda baja (IRAB) que recibieron SRNI en salas generales durante campañas de invierno de los años 2021-2022. SRNI incluyó cánula nasal de alto flujo (CNAF), presión positiva continua en la vía aérea (CPAP) o presión positiva en dos niveles (BiPAP). Cada evento se caracterizó individualmente, registrando el tipo de soporte y su respuesta clínica (éxito o fracaso). Fracaso se definió como la necesidad de traslado a UCIP. Entre estos grupos se compararon factores demográficos, características clínicas y tipos de soporte utilizados, mediante análisis bivariado y regresión logística multivariada. Resultados: De un total de 415 pacientes, el 14,2% fracasaron. Los principales predictores de fracaso fueron edad (OR ajustado = 0,87; IC 95%: 0,81-0,94), prematuridad (OR ajustado = 2,52; IC 95%: 1,07-5,96), presencia de cardiopatía congénita (OR ajustado = 5,92; IC 95%: 2,13-16,42) y una mayor frecuencia cardíaca (OR ajustado = 1,25; IC 95%: 1,06-1,48). Los desenlaces clínicos no variaron según el tipo de SRNI utilizado. En cuanto al tipo de SRNI utilizado, la CNAF fue utilizada en el 68,6% de los eventos, seguida por el CPAP (25,3%) y BiPAP (6,1%). Conclusiones: La aplicación de SRNI en salas de pediatría general previno la derivación a UCIP en 85.8% de los casos. Los factores de riesgo para derivación identificados incluyeron la menor edad, la prematuridad, la presencia de cardiopatía congénita y una frecuencia cardíaca inicial mayor. La CNAF emergió como la técnica de soporte respiratorio más empleada, seguida por el CPAP y el BiPAP, principalmente como estrategias de rescate a la primera.

Resumen La tasa de reintubación orotraqueal luego de la extubación se registra entre un 10 a 20%. La aplicación de soportes respiratorios no-invasivos (SRNI) posterior a la extubación como cánula nasal de alto-flujo, ventilación no invasiva (dos niveles de presión) y presión positiva continua en la vía aérea demostraron ser seguras y efectivas post extubación. El período pre-destete representa un momento crucial en el manejo de los pacientes críticos ya que el fracaso de la extubación, definido como la necesidad de reintubación dentro de los 2 a 7 días, demostró peores resultados al aumentar la mortalidad entre un 25-50%. Esta situación conlleva al requerimiento de ventilación mecánica prolongada, neumonía asociada a la ventilación mecánica y estancias prolongadas de internación. Por lo tanto, es esencial identificar a los pacientes que se beneficiarán utilizando SRNI post extubación.

Abstract The rate of re-intubation after extubation is recorded at 10-20%. The use of non-invasive respiratory support (NIRS) post-extubation such as high-flow nasal cannula, non-invasive ventilation (bilevel pressure) and continuous positive airway pressure (CPAP) have been shown to be safe and effective post-extubation. The pre-weaning period represents a crucial time in the management of critically ill patients, as extubation failure, defined as the need for re-intubation within 2-7 days, showed worse outcomes with mortality increasing by 25-50%. This situation leads to the requirement for prolonged mechanical ventilation, ventilator-associated pneumonia and long lengths of hospital stay. Therefore, it is essential to identify patients who will benefit from NIRS post extubation.

ABSTRACT With the increasing soybean production in Brazil, and the demand for soybeans with high protein and oil content, it is essential to conduct an in-depth study of the constituents of this grain, which can vary according to genotypes and growing conditions. Therefore, the objective of this study was to classify soybean genotypes, cultivated in different environments and sowing seasons, according to their chemical composition and the spectrum generated by near-infrared spectroscopy (NIRS). For this purpose, artificial intelligence and its machine learning technique were employed. 10 soybean genotypes were used, sown in two sowing seasons and cultivated 7 cities in Rio Grande do Sul. The chemical composition of the samples was analyzed using the FOSS NIRS DS2500 equipment, selecting the band between 807 and 817 nm. The applied algorithms were J48, Random Forest, CVR, lBk, MLP, using the Resample filter. The Weka software, version 3.8.6, was employed for data mining. The IBk algorithm achieved the best performance, reaching 89% correct classification of attributes. From the Confusion Matrix, it was observed that all genotypes obtained results above 60/70 for correctly predicted values, highlighting the algorithms’ good performance. In the metrics, IBk achieved 0.89 Precision, Recall, and F-Measure, and 0.94 ROC Area. Thus, it was possible to classify the genotypes according to their chemical composition related to the data obtained in the spectral curve, sowing season, and environment, using artificial intelligence and machine learning.

RESUMO Com a crescente produção de soja no Brasil e a demanda por grãos de soja com alto teor de proteína e óleo, é fundamental o estudo aprofundado dos constituintes desse grão, os quais podem variar de acordo com os genótipos e as condições de cultivo. Com isso, o objetivo desse estudo foi realizar a classificação de genótipos de soja, cultivados em diferentes ambientes e épocas de semeadura, de acordo com a composição química e o espectro gerado por infravermelho próximo (NIRs). Para isso, foi empregada a inteligência artificial e sua técnica de aprendizado de máquina. Foram utilizados 10 genótipos de soja, semeados em duas épocas de semeadura e em 7 cidades do Rio Grande do Sul. A composição química das amostras foi analisada através do equipamento FOSS NIRS DS2500, selecionando a banda entre 807 e 817nm. Os algoritmos aplicados foram J48, Random Forest, CVR, lBk, MLP, utilizando o filtro Resample. Foi empregado o software Weka, versão 3.8.6, para mineração de dados. O algoritmo IBk conseguiu o melhor desempenho, alcançando 89% de classificação correta dos atributos. A partir da Matriz de Confusão, observou-se que todos os genótipos obtiveram resultados superiores a 60/70 para os valores preditos corretamente, destacando o bom desempenho dos algoritmos. Nas métricas, o IBk obteve 0,89 de Precisão, Recall e F-Measure, e 0,94 de ROC Area. Foi possível classificar os genótipos, de acordo com a sua composição química relacionada aos dados obtidos na curva espectral, época e ambiente de semeadura, a partir da inteligência artificial e aprendizado de máquina.

Intracranial hemorrhage (ICH) is a serious medical condition that can lead to significant morbidity and mortality if not diagnosed and treated promptly. Early detection and treatment are essential for improving the outcome in patients with ICH. Near-infrared spectroscopy (NIRS) is a non-invasive imaging technique that has been used to detect changes in brain tissue oxygenation and blood flow in various conditions. The aim of this study was to investigate the predictive potential of NIRS for early diagnosis of ICH in patients presenting to the Emergency Department (ED) triage with headache. A total of 378 patients were included in the study. According to the final diagnosis of the patients, 4 groups were formed: migraine, tension-cluster headache, intracranial hemorrhage and intracranial mass, and control group. Cerebral NIRS values “rSO2” were measured at the first professional medical contact with the patient. The right and left rSO2 (RrSO2, LrSO2) were significantly lower and the rSO2 difference was significantly higher in the intracranial hemorrhage group compared to all other patient groups (P<0.001). The cut-off values determined in the receiver operating characteristics (ROC) analysis were RrSO2 ≤67, LrSO2 ≤67, and ΔrSO2 ≥9. This study found that a difference of more than 9 in cerebral right-left NIRS values can be a non-invasive, easy-to-administer, rapid, and reliable diagnostic test for early detection of intracranial bleeding. NIRS holds promise as an objective method in ED triage for patients with intracranial hemorrhage. However, further research is needed to fully understand the potential benefits and limitations of this method.

ABSTRACT: An experiment was conducted to assess the crop yield, nutritional composition, and mycotoxin contamination in different corn hybrids. The impact of these variables on the feed formulation cost of starter diets for broilers was also evaluated. A total of 150 samples from 50 corn hybrids was obtained from a field experiment. Nutrients were predicted by NIRS and mycotoxins were analyzed by HPLC-MS/MS. Data were submitted to analysis of variance and corn hybrids were grouped using Scott-Knott test at 5% significance. Pearson correlation analysis was performed among the main variables. Most of the variables were different among corn hybrids (P < 0.05), with exception of available phosphorus (Av. P), damaged grains, and DON and ZEA contaminations (P > 0.05). Crop yield had a positive correlation (P < 0.05) with feed formulation cost (r = 0.35) and a negative correlation (P < 0.05) with crude protein (-0.42), digestible (dig.) Thr (-0.42), and dig. Met+Cys (-0.42). The feed cost correlated positively (P < 0.05) with FUM (0.33) and ZEA (0.29), and negatively (P < 0.05) with crude protein (-0.74). Different corn hybrids vary in their productivity, nutritional content and mycotoxins contamination. It was demonstrated that an increase in the crop yield might be related to a reduction in corn nutritional content and quality, resulting in an increase in feed formulation cost.

RESUMO: Um experimento foi conduzido para avaliar a produtividade, a composição nutricional e a contaminação por micotoxinas em diferentes híbridos de milho. Essas medidas também foram correlacionadas com o impacto do custo do milho na formulação de ração inicial para frangos de corte. Um total de 150 amostras de 50 híbridos de milho foram obtidas em experimento de campo. As variáveis nutricionais foram preditas via NIRS e as micotoxinas foram analisadas por HPLC-MS/MS. Os dados foram submetidos à análise de variância e os híbridos agrupados pelo teste de Scott-Knott a 5% de significância. Correlação de Pearson foi realizada entre as principais variáveis. A maioria das variáveis foi diferente entre os híbridos de milho (P < 0,05), com exceção de fósforo disponível, grãos avariados e contaminações por DON e ZEA (P > 0,05). A produtividade teve correlação positiva (P < 0,05) com o custo de formulação da ração (r = 0,35) e correlação negativa (P < 0,05) com proteína bruta (-0,42), Tre digestível (dig.) (-0,42) e Met+Cis dig. (-0,42). O custo da formulação de rações apresentou correlação positiva (P < 0,05) com FUM (0,33) e ZEA (0,29), e negativa (P < 0,05) com proteína bruta (-0,74). Diferentes híbridos de milho variam em produtividade, conteúdo nutricional e contaminação por micotoxinas. Foi demonstrado que um aumento no rendimento à campo pode estar relacionado com redução no conteúdo nutricional e na qualidade do milho, resultando em um aumento no custo da formulação de ração.

ABSTRACT The use of near infrared spectroscopy (NIRS) as an alternative to the techniques commonly employed in the study of forage composition needs to be explored. The objective was to construct calibration curves to predict the physical-chemical composition of tropical grasses (Brachiaria brizantha (Hochst. ex A. Rich.) Stapf ‘Marandu’, ‘Piatã’; B. decumbens Stapf, Panicum maximum Jacq. ‘Colonião’), by NIRS and compare two multivariate regression method. Forage samples were analyzed for crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), ash, ether extract (EE), lignin, and moisture. The values obtained by the Official Methods of Analysis of Association of Official Agricultural Chemists (AOAC) were reference values for the creation of multivariate calibration models. The samples were scanned on the NIRS. The multivariate calibration models were created by the partial least squares (PLS) method and by the multiple linear regression (MLR) method. The predictive capacity of the models was evaluated by the correlation coefficient (R) and parameters of the mean squared deviation (RMSE). When the MLR was used, only the prediction model of ash (R = 0.82) of the P. maximum, EE (R = 0.87) and moisture (R = 0.90) of ‘Piatã’ showed approximate predictive capacity, for the other components R indicated good prediction. After the validation of the models developed by the PLS regression method, the CP (0.78-0.91), NDF (0.88-0.95), lignin (0.85-0.91), and moisture (0.79-0.96) predictions presented good results. The NIRS technique can be used to determine the physical-chemical composition of tropical grasses. The MLR multivariate regression method as well as PLS can be used to predict the physical-chemical composition of tropical grasses.

Resumen La espectroscopia de reflectancia en el infrarrojo cercano (NIRS) permite estimar la composición química de diversas muestras, (ingredientes, productos de origen animal, etc.); sin embargo, existe poca información de su uso con muestras de digesta ileal (DI) o heces (H) de cerdo; por lo que el objetivo fue desarrollar ecuaciones de predicción basadas en el método de cuadrados mínimos parciales para predecir la composición química de DI y H a través de un NIRS. Se utilizaron 110 muestras de DI y 202 de H de experimentos de digestibilidad, obteniéndose su espectro y mediante un modelo multivariado se desarrolló el método de predicción. Las variables analizadas en la DI fueron: proteína cruda (PC), Leucina (Leu), lisina (Lys) y treonina (Thr) y en las H: materia seca (MS), PC y energía (E). Los valores en DI fueron: PC: R2 0.98, error estándar de calibración (SEC) 0.330, error estándar de predicción (SEP) 0.640; Leu: R2 0.95, SEC 0.040, SEP 0.102; Lys: R2 0.93, SEC 0.077, SEP 0.143; Thr: R2 0.67, SEC 0.209, SEP 0.187. En H fueron PC: R2 0.98, SEC 0.95, SEP 1.19; E (kcal/kg): R2 0.94, SEC 60.8, SEP 95.3; MS: R2 0.87, SEC 0.83, SEP 1.15. Los resultados muestran que la robustez de la calibración (DE/SEP) fue buena para PC, 3.34, Leu 2.07 y Lys 2.48 y regular para Thr 1.94, la predicción (RPD) fue buena para PC 2.11 en la DI. En H las R2 fueron altas para PC 0.98 y E 0.94. La mayor robustez fue para PC 5.59 y su predicción fue excelente 4.16 y buena para E 2.53. Se concluye que el NIRS puede predecir la PC en la DI y la PC y E en H. Para mejorar la estimación de aminoácidos en DI deben explorarse las causas que afectan la robustez de las calibraciones.

Abstract Near-infrared reflectance spectroscopy (NIRS) allows the estimation of the chemical composition of various samples (ingredients, animal products, etc.); however, there is little information on its use with samples of swine ileal digesta (ID) or feces (F). Therefore, the objective was to develop prediction equations based on the partial least squares method to predict the chemical composition of the ID and F through NIRS. 110 ID and 202 F samples from digestibility experiments were used, their spectra were obtained and a multivariate model was used to develop the prediction method. The variables analyzed in ID were: crude protein (CP), leucine (Leu), lysine (Lys) and threonine (Thr) and in F: dry matter (DM), CP and energy (E). The DI values were: PC: R2 0.98, standard error of calibration (SEC) 0.330, standard error of prediction (SEP) 0.640; Leu: R2 0.95, SEC 0.040, SEP 0.102; Lys: R2 0.93, SEC 0.077, SEP 0.143; Thr: R2 0.67, SEC 0.209, SEP 0.187. F values were: PC: R2 0.98, SEC 0.95, SEP 1.19; E (kcal/kg): R2 0.94, SEC 60.8, SEP 95.3; MS: R2 0.87, SEC 0.83, SEP 1.15. The results show that the calibration robustness (SD/SEP) was good for PC, 3.34, Leu 2.07 and Lys 2.48 and fair for Thr 1.94, the prediction (RPD) was good for PC 2.11 in ID. In F the R2 were high for PC 0.98 and E 0.94. The highest robustness was for PC 5.59 and its prediction was excellent 4.16 and good for E 2.53. It is concluded that NIRS can predict PC in ID and PC and E in F. In order to improve the estimation of amino acids in ID, the causes affecting the robustness of the calibrations should be explored.

Abstract This technical note sought to examine the ability of near-infrared reflectance spectroscopy (NIRS) to predict the chemical content and organic matter digestibility (OMD) of whole plants and the morphological components of forage sunflower. Empirical models for the prediction of OMD values from chemical components were developed, and their predictive ability vs. NIRS models was assessed. The total set of samples (n=147) was composed of whole plants (n=14) and morphological components (n=133) from different experiments performed at Galicia (Spain) and were scanned using a Foss NIR System 6500 instrument. The reference values of OMD corresponded to in vitro determinations (n=112 samples) from laboratory incubation tests using rumen fluid. The predictive capacity of the NIRS models was assessed by the coefficient of determination value in external validation (r2), showing good to excellent quality prediction of OMD and chemical components with values of r2≥0.88. However, the estimation of lignin did not show predictive utility (r2=0.40). Using the NIRS models to predict the OMD of whole plants and morphological components of forage sunflower led to a decrease in the standard error in external validation, in contrast to the best empirical equation through the chemical components of samples (from ±8.25 to ±3.23%). This technical note showed that NIRS is a suitable technology, providing a rapid assessment of forage sunflower. However, these results should be considered preliminary, as they are based on a limited number of samples, and it is desirable to improve the performance of NIRS equations by increasing the dataset in future works.

Resumen En esta nota técnica se evalúa la capacidad de la técnica NIRS para estimar la composición química y la digestibilidad de la materia orgánica de la planta entera de girasol (n=14) y sus componentes morfológicos (n=133) aprovechado para forraje, y se desarrollan ecuaciones empíricas basadas en parámetros químicos para la estimación de la digestibilidad de la materia orgánica (DMO), comparando su capacidad predictiva con la obtenida mediante NIRS. La información espectral de las muestras secas y molidas se realizó en un instrumento Foss NIRSystem 6500. Los valores de referencia de DMO (n=112) corresponden a determinaciones mediante incubaciones in vitro con líquido ruminal. Las calibraciones NIRS obtenidas para la predicción de todos los parámetros mostraron valores del coeficiente de determinación en validación externa (r2) de calidad buena a excelente para la predicción de DMO y composición química, con valores de r2 iguales o superiores a 0.88. Sin embargo, la ecuación NIRS de estimación del contenido en lignina no mostró utilidad predictiva (r2=0.40). La tecnología NIRS mostró una superioridad predictiva de la DMO, comparada con la mejor ecuación empírica, permitiendo reducir el error de predicción de validación externa, desde ±8.25% a ±3.23%. Se concluye que las ecuaciones NIRS desarrolladas son una herramienta útil para la evaluación nutricional rápida y precisa de la planta entera de girasol y sus componentes morfológicos. Sin embargo, estos resultados deben considerarse como preliminares, ya que se basan en un número limitado de muestras y es deseable mejorar la robustez de las ecuaciones NIRS mediante el incremento de la colección de muestras.

RESUMEN La espectroscopía de reflectancia en el infrarrojo cercano (NIRS) es una tecnología rápida, multiparamétrica, amigable con el ambiente, de bajo costo y gran exactitud, para el análisis de diversos componentes en alimentos, en suelo y en agricultura. El objetivo del presente estudio fue construir modelos de calibración NIRS, para la predicción de nutrientes en tejido vegetal de caña de azúcar, para producción de panela, cultivada en la región de la Hoya del río Suárez. Un total de 416 muestras de tejido fueron escaneadas en el segmento espectral Vis-NIR. El análisis quimiométrico, se realizó con el software WinISI V4.10, aplicando la regresión de mínimos cuadrados parciales modificados, junto a una validación cruzada. Se evaluaron cuatro modelos con diferentes tratamientos matemáticos y el rendimiento de las calibraciones, se hizo por medio de la validación externa, analizando las medidas de bondad de ajuste, como el coeficiente de determinación de la predicción, el error estándar de la predicción ajustado por el sesgo y la desviación predictiva residual. Los resultados muestran que el modelo de calibración para N presentó el mayor poder predictivo. Para macronutrientes, las calibraciones, con mayor poder predictivo, fueron P y K y para micronutrientes, el modelo para B, mientras que para Cu presentó el más bajo poder predictivo. Se encontraron modelos adecuados para la predicción de los contenidos de N, Ca y P; para los demás nutrientes, se recomienda ampliar el conjunto de calibración.

ABSTRACT Near Infrared Reflectance Spectroscopy (NIRS) is a fast, multiparametric, environmentally friendly, low-cost, and highly accurate technology for the analysis of components in food, soil, and agriculture. The purpose of this study was to generate NIRS calibration models for the prediction of nutrients in plant tissue of sugarcane to panela production cultivated in the Hoya del Río Suárez region. A total of 416 tissue samples were scanned in Vis-NIR spectral segment. Chemometric analysis was performed with the WinISI V4.10 software applying modified partial least squares regression with cross-validation. Four models with different mathematical treatments were evaluated, and the performance of calibrations was made through external validation analyzing the goodness-of-fit measures as prediction determination coefficient, standard error of the bias-adjusted prediction, and residual predictive deviation. The results showed that the calibration model for N had the highest predictive power. For macronutrients, the calibrations with the best predictive power were for P and K, and micronutrients for B, while Cu presented the lowest predictive power. Adequate models were found for the prediction of N, Ca, and P. In the case of the other nutrients, it is recommended to expand the calibration set.

Resumen: La espectroscopia cercana al infrarrojo (NIRS, por sus siglas en inglés) es un método óptico de diagnóstico no invasivo, útil como guía para tomar medidas correctivas y prevenir así la lesión cerebral en prematuros. El objetivo es evaluar la eficacia de la NIRS en la detección de la oxigenación cerebral y su impacto en la prevención de lesiones cerebrales y la mortalidad en prematuros. Se realizó una búsqueda sistemática de la literatura en PubMed y Cochrane con las palabras clave spectroscopy, near-infrared, preterm newborn, infant, neonates, Clinical Trial, Meta-Analysis, systematic para encontrar estudios que evalúen el uso de la NIRS en la oxigenación cerebral en recién nacidos pretérmino. Se incluyó un ensayo clínico fase II, uno fase III y una revisión sistemática. En estos estudios el uso de la NIRS redujo de manera significativa la carga de hipoxia cerebral. La NIRS mostró tendencias en reducir la mortalidad y la lesión cerebral, pero sin diferencia estadísticamente significativa.

Abstract: Near-infrared spectroscopy (NIRS) is a non-invasive optical diagnostic method as a guide to taking corrective measures to prevent brain injury in premature infants. This review aims to evaluate the efficacy of NIRS in detecting cerebral oxygenation and its impact on preventing brain injury and mortality in preterm. A systematic search of the literature was carried out in PubMed and Cochrane with the keywords spectroscopy, near-infrared, preterm newborn, infant, neonates, Clinical Trial, Meta-Analysis, and systematic to find studies that evaluate the use of the NIRS in cerebral oxygenation in preterm newborns. A phase II clinical trial, a phase III clinical trial, and a systematic review were included. NIRS significantly reduced the burden of cerebral hypoxia. The NIRS showed trends in reducing mortality and brain injury but did not show a statistically significant difference.

Resumen: La estenosis carotídea (EC) ocurre en 13% de los pacientes con estenosis valvular aórtica (EVA). El riesgo de evento vascular cerebral (EVC), en los pacientes con EC significativa sometidos a cirugía valvular cardíaca, puede aumentar hasta 11%. Someter a un paciente con EVA crítica y fracción de eyección del ventrículo izquierdo (FEVI) disminuida a endarterectomía carotídea es todo un reto anestésico, cuyo principal objetivo es evitar la hipotensión y el bajo gasto cardíaco. La anestesia regional es una opción para estos pacientes. Presentamos el caso de un hombre de 70 años con diagnóstico de EC significativa y EVA crítica con disfunción ventricular izquierda, al que se realizó endarterectomía carotídea con bloqueo del plexo cervical superficial por alto riesgo de colapso circulatorio. Dicha estrategia anestésica permitió mantener al paciente despierto durante la cirugía, al valorar continuamente su estado neurológico. Asimismo, se documentaron los cambios transoperatorios en el NIRS (Near-infrared spectroscopy) cerebral y Doppler transcraneal (DTC), los cuales se correlacionaron con el estado clínico del paciente. En un segundo tiempo se hizo cambio valvular aórtico sin complicaciones. En este caso destaca la importancia de la anestesia regional y el monitoreo neurológico con Doppler transcraneal, en pacientes sometidos a endarterectomía carotídea con alto riesgo quirúrgico por EVA crítica.

Abstract: Carotid stenosis occurs in 13% of patients with aortic valve stenosis. The risk of stroke in patients with significant carotid stenosis undergoing heart valve surgery may increase to 11%. Proposing a patient with critical aortic valve stenosis and left ventricular dysfunction to carotid endarterectomy is an anesthetic challenge, where the objective is to avoid hypotension and low cardiac output. Regional anesthesia is an option for these patients. Due to the high incidence of intraoperative stroke during carotid endarterectomy, continuous neurological monitoring is of relevance. We present the case of a 70-year-old man diagnosed with significant carotid stenosis and critical aortic valve stenosis and left ventricular dysfunction who underwent carotid endarterectomy with superficial cervical plexus block due to a high risk of circulatory collapse. In addition, this anesthetic strategy made it possible to keep the patient awake during surgery, and to continuously assess their neurological status. Likewise, transoperative changes in brain NIRS and transcranial Doppler were documented, which correlated with the patient’s clinical status. In a second time, aortic valve replacement was performed without complications. This case highlights the importance of regional anesthesia and neurological monitoring in patients undergoing carotid endarterectomy with high surgical risk due to critical aortic valve stenosis.

Abstract Near infrared spectroscopy (NIRS) combined with various chemometrics methods was tried to identify the fresh tea leaves at different altitudes quickly and nondestructively. Three kinds of samples were collected, then scanning NIRS, conducting spectral preprocessing to remove noise information, using backward interval partial least squares to screen characteristic spectral intervals, going on principal component analysis, respectively. Finally, least squares support vector machine method (LS-SVM) was applied to establish NIRS models, whose robustness was tested by prediction set samples. The best pretreated method was the combination of multivariate scattering correction and the first derivative. Six characteristic spectral intervals were screened, and the corresponding spectral wavenumbers were 4821.2-5091.2 cm-1, 5368.9-5638.8 cm-1, 6190.4-6460.4 cm-1, 7011.9-7281.9 cm-1, 8924.9-9191.1 cm-1 and 9734.9-10000 cm-1. The cumulative contribution rate of the first three principal components was 99.92%. The root mean square error of the cross validation and the determination coefficient of the calibration set model were 0.027 and 0.973, respectively. The root mean square error and the determination coefficient of the prediction set model were 0.034 and 0.968, respectively. The discrimination accuracy in prediction set was 100%. The results showed NIRS combined with LS-SVM can realize fast and nondestructive discrimination of fresh tea leaves at different altitudes.

Abstract The current developmental trend is to evaluate the quality of Yongchuan Xiuya tea rapidly. After spectrum pre-processing, near infrared spectroscopy (NIRS) coupled with synergy interval partial least squares (siPLS), principal component analysis (PCA) and back propagation-artificial neural network (BP-ANN) was applied to rapidly and non-destructively predict the quality of Yongchuan Xiuya tea. External Yongchuan Xiuya tea samples were used for the actual application of the proposed model. The best pre-processing method was multiple scattering correction coupled with second derivative, and the characteristic spectral regions selected by siPLS were 4381.5-4755.6 cm-1, 4759.5-5133.6 cm-1, 6266.6-6637.8 cm-1 and 7389.9-7760.2 cm-1. The cumulative contribution rate was 99.05% for the first three principal components of the characteristic spectra regions. The transfer function, root mean square error and determinant coefficient of the best BP-ANN prediction model were the tanh function, 0.384 and 0.977, respectively. The root mean square error and determinant coefficient of the external 10 Yongchuan Xiuya tea samples were 0.406 and 0.969, respectively. These results showed that NIRS combined with BP-ANN algorithm can be used to evaluate the quality of Yongchuan Xiuya tea rapidly and accurately.

ABSTRACT Purpose: In this study, the aim to assess the combined effects of prone-positioning (PP) and minimal-flow (MF) general anesthesia on regional cerebral oxygenation (RCO) and systemic hemodynamics. Methods: This is a randomized prospective study aiming to evaluate changes in cerebral oxygenation and hemodynamic parameters in MF systemic anesthesia in patients undergoing surgery in PP. Patients were randomized to MF or normal-flow (NF) anesthesia. In the operating room, pulse rate, mean arterial pressure (MAP), peripheral hemoglobin oxygen saturation (spO2), and right- and left-side RCO (assessed by near-infrared spectroscopy, NIRS) were measured perioperatively. Results: Overall, 46 patients were included (24 in the MF group and 22 in the NF group). The amount of anesthetic gas consumption was significantly lower in the low-flow (LF) group. In both groups, the mean pulse rate showed a decrease after PP. Before induction, RCO was significantly higher both at the right- and left-sides in the LF group compared to the NF group. This difference continued throughout the operation on the left-side and disappeared 10 min after intubation on the right-side. On the left side, mean RCO decreased after PP in both groups. Conclusions: MF anesthesia in PP did not reduce cerebral oxygenation compared to NF and was safe in terms of systemic hemodynamics and cerebral oxygenation. Purpose pronepositioning prone positioning (PP minimalflow minimal flow (MF (RCO Methods normalflow normal (NF room MAP, MAP , (MAP) spO2, spO2 spO (spO2) right leftside side assessed nearinfrared near infrared spectroscopy NIRS perioperatively Results Overall 4 24 (2 2 . group) lowflow low (LF groups induction leftsides sides 1 rightside. rightside side. right-side Conclusions (MAP (spO2 ( (spO

ABSTRACT Biochar soil amendments are attracting attention as one strategy to improve soil microbially ecological environment and regulate the soil nitrogen cycle. This study aimed to evaluate the effects of biochar application on agricultural soil improvement, nitrogen (N) mineralization and nitrification. The experiment was carried out on a typical farmland containing black soil and saline-alkaline soil in Northeast China. Four treatments were undertaken, including the control-treated black soil farmland (CS), the biochar-treated black soil farmland (BCS), the control-treated saline-alkali soil farmland (SAS), and the biochar-treated saline-alkaline soil farmland (BSAS). Basic physical and chemical properties, enzyme activity, and the contents of ammonium-nitrogen (NH4+-N) and nitrate-nitrogen (NO3--N) in the soil were subsequently determined. The co-occurrence networks of bacterial communities of the biochar and control treatment groups were constructed based on high-throughput sequencing data of the 16S rRNA genes. The results showed that the BCS and BSAS treatments significantly increased the contents of soil organic matter, total nitrogen, total phosphorus, and available phosphorus. The application of biochar significantly increased the NH4+-N contents in the black soil and saline-alkaline soil by 81.78 and 80.08 %, respectively, while significantly reducing the soil NH4+-N/NO3--N content, which promoted the transformation of NH4+-N into NO3--N. Subsequently, the released NH4+-N was transformed into NO3--N through nitrification. After the biochar application, the NO3--N contents in the black and saline-alkaline soils could be fixed. The biochar application significantly increased the abundance of gdh, AOA-amoA, AOB-amoA, nirK, nirS, nosZ, and nifH genes, with no significant difference in the abundance of napA genes being found among different treatments. Microbes playing a key role in the co-occurrence network were Proteobateria, Acidobacteria, Bacteroidetes, Actinobacteria, and Chloroflexi. As compared with the CS and SAS treatments, under the BCS+BSAS treatment, the connectors, module hubs, connectedness, and clustering coefficient showed larger parameters, and the networks were more complex. The application of biochar gradually increased the nodes, edges, and average degree of the bacterial co-occurrence network, thus indicating that the interaction between microbial groups in the black and saline-alkaline soils post biochar application may be important in the biogeochemical cycle process in farmland soil. improvement N (N nitrification salinealkaline saline alkaline China undertaken controltreated treated CS, , (CS) biochartreated BCS, (BCS) salinealkali alkali SAS, (SAS) BSAS. . (BSAS) properties activity ammoniumnitrogen ammonium NH4+N NH4N NHN NH4+ NH4 NH (NH4+-N nitratenitrogen nitrate NO3N NON NO3 NO (NO3--N determined cooccurrence co occurrence highthroughput high throughput S matter phosphorus 8178 81 78 81.7 8008 80 08 80.0 % respectively NH4+N/NO3N NH4NNO3N NHNNON N/NO3 content NO3N. N. Subsequently fixed gdh AOAamoA, AOAamoA AOA amoA, amoA AOA-amoA AOBamoA, AOBamoA AOB AOB-amoA nirK nirS nosZ Proteobateria Acidobacteria Bacteroidetes Actinobacteria Chloroflexi BCSBSAS connectors hubs connectedness parameters complex nodes edges (CS (BCS (SAS (BSAS 817 8 7 81. 800 0 80. NNO NNO3 N/NO