Taste intensity and hedonic responses to simple beverages in gastrointestinal cancer patients

Changes in the taste of food have been implicated as a potential cause of reduced dietary intake among cancer patients. However, data on intensity and hedonic responses to the four basic tastes in cancer are scanty and contradictory. The present study aimed at evaluating taste intensity and hedonic responses to simple beverages in 47 anorectic patients affected by gastrointestinal cancer and in 55 healthy subjects. Five suprathreshold concentrations of each of the four test substances (sucrose in black current drinks, citric acid in lemonade, NaCl in unsalted tomato juice, and urea in tonic water) were used. Patients were invited to express a judgment of intensity and pleasantness ranging from 0 to 10. Mean intensity scores directly correlated with concentrations of sour, salty, bitter, and sweet stimuli, in both normals and those with cancer. Intensity judgments were higher in cancer patients with respect to sweet (for median and high concentrations, P < 0.05), salty (for all concentrations, P < 0.05), and bitter tastes (for median concentration, P < 0.01). Hedonic function increased with the increase of the stimuli only for the sweet taste. A negative linear correlation was found between sour, bitter, and salty concentrations and hedonic score. Both in cancer patients and in healthy subjects, hedonic judgments increased with the increase of the stimulus for the sweet taste (r 1/4 0.978 and r 1/4 0.985, P 1/4 0.004 and P 1/4 0.002, respectively), and decreased for the salty (r 1/4 ??0.827 and r 1/4 ??0.884, P 1/4 0.084 and P 1/4 0.047, respectively) and bitter tastes (r 1/4 ??0.990 and r 1/4 ??0.962, P 1/4 0.009 and P 1/4 0.001, respectively). For the sour taste, the hedonic scores remained stable with the increase of the stimulus in noncancer controls (r 1/4 ??0.785, P 1/4 0.115) and decreased in cancer patients (r 1/4 ??0.996, P 1/4 0.0001). The hedonic scores for the sweet taste and the bitter taste were similar in cancer patients and healthy subjects, and these scores were significantly higher in cancer patients than in healthy subjects for most of the concentrations of the salty taste and all the concentrations of the sour taste. The present study suggests that cancer patients, compared to healthy individuals, have a normal sensitivity, a normal likingfor pleasant stimuli, and a decreased dislike for unpleasant stimuli. Moreover, when compared to controls, they show higher hedonic scores for middle and high concentrations of the salty taste and for all concentrations of the sour taste. Further studies are needed to evaluate whether these changes observed in cancer patients translate into any alteration in dietary behavior and/or food preferences

Non-invasive and label-free identification of human natural killer cell subclasses by biophysical single-cell features in microfluidic flow

Natural killer (NK) cells are indicated as favorite candidates for innovative therapeutic treatment and are divided into two subclasses: immature regulatory NK CD56(bright) and mature cytotoxic NK CD56(dim). Therefore, the ability to discriminate CD56(dim) from CD56(bright) could be very useful because of their higher cytotoxicity. Nowadays, NK cell classification is routinely performed by cytometric analysis based on surface receptor expression. Here, we present an in-flow, label-free and non-invasive biophysical analysis of NK cells through a combination of light scattering and machine learning (ML) for NK cell subclass classification. In this respect, to identify relevant biophysical cell features, we stimulated NK cells with interleukine-15 inducing a subclass transition from CD56(bright) to CD56(dim). We trained our ML algorithm with sorted NK cell subclasses (&gt;= 86% accuracy). Next, we applied our NK cell classification algorithm to cells stimulated over time, to investigate the transition of CD56(bright) to CD56(dim) and their biophysical feature changes. Finally, we tested our approach on several proband samples, highlighting the potential of our measurement approach. We show a label-free way for the robust identification of NK cell subclasses based on biophysical features, which can be applied in both cell biology and cell therapy

Non-invasive and label-free identification of human natural killer cell subclasses by biophysical single-cell features in microfluidic flow

Natural killer (NK) cells are indicated as favorite candidates for innovative therapeutic treatment and are divided into two subclasses: immature regulatory NK CD56bright and mature cytotoxic NK CD56dim. Therefore, the ability to discriminate CD56dim from CD56bright could be very useful because of their higher cytotoxicity. Nowadays, NK cell classification is routinely performed by cytometric analysis based on surface receptor expression. Here, we present an in-flow, label-free and non-invasive biophysical analysis of NK cells through a combination of light scattering and machine learning (ML) for NK cell subclass classification. In this respect, to identify relevant biophysical cell features, we stimulated NK cells with interleukine-15 inducing a subclass transition from CD56bright to CD56dim. We trained our ML algorithm with sorted NK cell subclasses (≥86% accuracy). Next, we applied our NK cell classification algorithm to cells stimulated over time, to investigate the transition of CD56bright to CD56dim and their biophysical feature changes. Finally, we tested our approach on several proband samples, highlighting the potential of our measurement approach. We show a label-free way for the robust identification of NK cell subclasses based on biophysical features, which can be applied in both cell biology and cell therapy