Validazione e applicazioni cliniche della metodica NIRA (Near Infrared Reflectance Analysis) per la valutazione del contenuto nutrizionale del latte materno in terapia intensiva neonatale

Phase 1: To validate Near-Infrared Reflectance Analysis (NIRA) as a fast, reliable and suitable method for routine evaluation of human milk’s nitrogen and fat content. Phase 2: To determine whether fat content, protein content and osmolality of HM before and after fortification may affect gastroesophageal reflux (GER) in symptomatic preterm infants. Patients and Methods: Phase 1: 124 samples of expressed human milk (55 from preterm mothers and 69 from term mothers) were used to validate NIRA against traditional methods (Gerber method for fat and Kjeldhal method for nitrogen). Phase 2: GER was evaluated in 17 symptomatic preterm newborns fed naïve and fortified HM by combined pH/intraluminal-impedance monitoring (pH-MII). HM fat and protein content was analysed by a Near-Infrared-Reflectance-Analysis (NIRA). HM osmolality was tested before and after fortification. GER indexes measured before and after fortification were compared, and were also related with HM fat and protein content and osmolality before and after fortification. Results: Phase 1: · A strong agreement was found between traditional methods’ and NIRA’s results (expressed as g/100 g of milk), both for fat and nitrogen content in term (mean fat content: NIRA=2.76; Gerber=2.76; mean nitrogen content: NIRA=1.88; Kjeldhal =1.92) and preterm (mean fat content: NIRA=3.56; Kjeldhal=3.52; mean nitrogen content: NIRA=1.91; Kjeldhal =1.89) mother’s milk. · Nitrogen content of the milk samples, measured by NIRA, ranged from 1.18 to 2.71 g/100 g of milk in preterm milk and from 1.48 to 2.47 in term milk; fat content ranged from 1.27 to 6.23 g/100 g of milk in preterm milk and from 1.01 to 6.01 g/100 g of milk in term milk. Phase 2: · An inverse correlation was found between naïve HM protein content and acid reflux index (RIpH: p=0.041, rho=-0.501). · After fortification, osmolality often exceeded the values recommended for infant feeds; furthermore, a statistically significant (p<.05) increase in non acid reflux indexes was observed. Conclusions: NIRA can be used as a fast, reliable and suitable tool for routine monitoring of macronutrient content of human milk. Protein content of naïve HM may influence acid GER in preterm infants. A standard fortification of HM may worsen non acid GER indexes and, due to the extreme variability in HM composition, may overcome both recommended protein intake and HM osmolality. Thus, an individualized fortification, based on the analysis of the composition of naïve HM, could optimize both nutrient intake and feeding tolerance.

New techniques for the remote sensing of foliar nitrogen concentration in forest ecosystems.

During my Doctoral study I researched about the remote detection of canopy N concentration in forest stands, its potentials and problems, under many overlapping perspectives. The study consisted of three parts. In S. Rossore 2000 dataset analysis, I tested regressions between N concentration and NIR reflectances derived from different sources (field samples, airborne and satellite sensors). The analysis was further expanded using a larger dataset acquired in year 2009 as part of a new campaign funded by the ESA. In both cases, a good correlation was observed between Landsat NIR, using both TM (2009) and ETM+ (2000) imagery, and N concentration measured by a CHN elemental analyzer. Concerning airborne sensors I did not obtain the same good results, mainly because of the large FOV of the two instruments, and to the anisotropy of vegetation reflectance. We also tested the relation between ground based ASD measures and nitrogen concentration, obtaining really good results. Thus, I decided to expand my study to the regional level, focusing only on field and satellite measures. I analyzed a large dataset for the whole of Catalonia, Spain; MODIS imagery was used, in consideration of its spectral characteristics and despite its rather poor spatial resolution. Also in this case a regression between nitrogen concentration and reflectances was found, but not so good as in previous experiences. Moreover, vegetation type was found to play an important role in the observed relationship. We concluded that MODIS is not the most suitable satellite sensor in realities like Italy and Catalonia, which present a patchy and inhomogeneous vegetation cover; so it could be utilized for the parameterization of eco-physiological and biogeochemical models, but not for really local nitrogen estimate. Thus multispectral sensors similar to Landsat Thematic Mapper, with better spatial resolution, could be the most appropriate sensors to estimate N concentration.