Non-invasive assessment of sublingual microcirculation using flow derived from green light PPG: evaluation and reference values.

Significance: The study of sublingual microcirculation offers valuable insights into vascular changes and overcomes some limitations of peripheral microcirculation assessment. Videomicroscopy and pulse oximetry have been used to assess microcirculation, providing insights into organ perfusion beyond macrohemodynamics parameters. However, both techniques have important limitations that preclude their use in clinical practice. Aim: To address this, we propose a non-invasive approach using photoplethysmography (PPG) to assess microcirculation. Approach: Two experiments were performed on different samples of 31 subjects. First, multi-wavelength, finger PPG signals were compared before and while applying pressure on the sensor to determine if PPG signals could detect changes in peripheral microcirculation. For the second experiment, PPG signals were acquired from the ventral region of the tongue, aiming to assess the microcirculation through features calculated from the PPG signal and its first derivative. Results: In experiment 1, 13 out of 15 features extracted from green PPG signals showed significant differences (p<0.05) before and while pressure was applied to the sensor, suggesting that green light could detect flow distortion in superficial capillaries. In experiment 2, 15 features showed potential application of PPG signal for sublingual microcirculation assessment. Conclusions: The PPG signal and its first derivative have the potential to effectively assess microcirculation when measured from the fingertip and the tongue. The assessment of sublingual microcirculation was done through the extraction of 15 features from the green PPG signal and its first derivative. Future studies are needed to standardize and gain a deeper understanding of the evaluated features.

Comparación de dos tipos de clasificación en dispepsia funcional; distrés postprandial o dolor epigástrico vs. un análisis multidimensional por conglomerados utilizando aprendizaje no supervisado

Los métodos de inteligencia artificial utilizando herramientas de aprendizaje no supervisado pueden apoyar la resolución de problemas al establecer patrones de agrupación o clasificación no identificados, que permiten tipificar subgrupos para manejos más individualizados. Existen pocos estudios que permiten conocer la influencia de síntomas digestivos y extradigestivos en la tipificación dispepsia funcional; esta investigación realizó un análisis de aprendizaje no supervisado por conglomerados basándose en dichos síntomas, para discriminar subtipos de dispepsia y comparar con una de las clasificaciones actualmente más aceptadas. Se realizó un análisis exploratorio de conglomerados en adultos con dispepsia funcional según síntomas digestivos, extradigestivos y emocionales. Se conformaron patrones de agrupación de tal manera que dentro de cada grupo existiera homogeneidad en cuanto a los valores adoptados por cada variable. El método de análisis de conglomerados fue bietápico y los resultados del patrón de clasificación se compararon con una de las clasificaciones más aceptadas de dispepsia funcional. De 184 casos, 157 cumplieron con criterios de inclusión. El análisis de conglomerados excluyó 34 casos no clasificables. Los pacientes con dispepsia de tipo 1 (conglomerado uno), presentaron mejoría al tratamiento en el 100% de los casos, solo una minoría presentaron síntomas depresivos. Los pacientes con dispepsia de tipo 2 (conglomerado dos) presentaron una mayor probabilidad de falla al tratamiento con inhibidor de bomba de protones, padecieron con mayor frecuencia trastornos de sueño, ansiedad, depresión, fibromialgia, limitaciones físicas o dolor crónico de naturaleza no digestiva. Esta clasificación de dispepsia por análisis de clúster establece una visión más holística de la dispepsia en la cual características extradigestivas, síntomas afectivos, presencia o no de trastornos de sueño y de dolor crónico permiten discriminar el comportamiento y respuesta al manejo de primera línea.

Artificial intelligence methods using unsupervised learning tools can support problem solving by establishing unidentified grouping or classification patterns that allow typing subgroups for more individualized management. There are few studies that allow us to know the influence of digestive and extra-digestive symptoms in the classification of functional dyspepsia. This research carried out a cluster unsupervised learning analysis based on these symptoms to discriminate subtypes of dyspepsia and compare with one of the currently most accepted classifications. An exploratory cluster analysis was carried out in adults with functional dyspepsia according to digestive, extra-digestive and emotional symptoms. Grouping patterns were formed in such a way that within each group there was homogeneity in terms of the values adopted by each variable. The cluster analysis method was two-stage and the results of the classification pattern were compared with one of the most accepted classifications of functional dyspepsia. Of 184 cases, 157 met the inclusion criteria. The cluster analysis excluded 34 unclassifiable cases. Patients with type 1 dyspepsia (cluster one) presented improvement after treatment in 100% of cases, only a minority presented depressive symptoms. Patients with type 2 dyspepsia (cluster two) presented a higher probability of failure to treatment with proton pump inhibitor, suffered more frequently from sleep disorders, anxiety, depression, fibromyalgia, physical limitations or chronic pain of a non-digestive nature. This classification of dyspepsia by cluster analysis establishes a more holistic vision of dyspepsia in which extradigestive characteristics, affective symptoms, presence or absence of sleep disorders and chronic pain allow discriminating behavior and response to first-line management.

Development and characterization of a field-effect biosensor for the detection of acetoin.

A capacitive electrolyte-insulator-semiconductor (EIS) field-effect biosensor for acetoin detection has been presented for the first time. The EIS sensor consists of a layer structure of Al/p-Si/SiO2/Ta2O5/enzyme acetoin reductase. The enzyme, also referred to as butane-2,3-diol dehydrogenase from B. clausii DSM 8716T, has been recently characterized. The enzyme catalyzes the (R)-specific reduction of racemic acetoin to (R,R)- and meso-butane-2,3-diol, respectively. Two different enzyme immobilization strategies (cross-linking by using glutaraldehyde and adsorption) have been studied. Typical biosensor parameters such as optimal pH working range, sensitivity, hysteresis, linear concentration range and long-term stability have been examined by means of constant-capacitance (ConCap) mode measurements. Furthermore, preliminary experiments have been successfully carried out for the detection of acetoin in diluted white wine samples.