Tracing the identity of Parmigiano Reggiano "Prodotto di Montagna - Progetto Territorio" cheese using NMR spectroscopy and multivariate data analysis

BACKGROUND

Nuclear magnetic resonance (NMR) spectroscopy is one of the well-established tools for food metabolomic analysis, as it proved to be very effective in authenticity and quality control of dairy products, as well as to follow product evolution during processing and storage. The analytical assessment of the EU mountain denomination label, specifically for Parmigiano Reggiano "Prodotto di Montagna - Progetto Territorio" (Mountain-CQ) cheese, has received limited attention. Although it was established in 2012 the EU mountain denomination label has not been much studied from an analytical point of view. Nonetheless, tracing a specific profile for the mountain products is essential to support the value chain of this specialty.

RESULTS

The aim of the study was to produce an identity profile for Parmigiano Reggiano "Prodotto di Montagna - Progetto Territorio" (Mountain-CQ) cheese, and to differentiate it from Parmigiano Reggiano PDO samples (conventional-PDO) using 1H NMR spectroscopy coupled with multivariate data analysis. Three different approaches were applied and compared. First, the spectra-as-such were analysed after proper preprocessing. For the other two approaches, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) was used for signals resolution and features extraction, either individually on manually-defined spectral intervals or by reapplying MCR-ALS on the whole spectra with selectivity constraints using the reconstructed "pure profiles" as initial estimates and targets. All approaches provided comparable information regarding the samples' distribution, as in all three cases the separation between the two product categories conventional-PDO and Mountain-CQ could be highlighted. Moreover, a novel MATLAB toolbox for features extraction via MCR-ALS was developed and used in synergy with the Chenomx library, allowing for a putative identification of the selected features.

SIGNIFICANCE

A first identity profile for Parmigiano Reggiano "Prodotto di Montagna - Progetto Territorio" obtained by interpreting the metabolites signals in NMR spectroscopy was obtained. Our workflow and toolbox for generating the features dataset allows a more straightforward interpretation of the results, to overcome the limitations due to dimensionality and to peaks overlapping, but also to include the signals assignment and matching since the early stages of the data processing and analysis.

VECTOR: An algorithm for the detection of COVID-19 pneumonia from velcro-like lung sounds

The coronavirus disease 2019 (COVID-19) has severely stressed the sanitary systems of all countries in the world. One of the main issues that physicians are called to tackle is represented by the monitoring of pauci-symptomatic COVID-19 patients at home and, generally speaking, everyone the access to the hospital might or should be severely reduced. Indeed, the early detection of interstitial pneumonia is particularly relevant for the survival of these patients. Recent studies on rheumatoid arthritis and interstitial lung diseases have shown that pathological pulmonary sounds can be automatically detected by suitably developed algorithms. The scope of this preliminary work consists of proving that the pathological lung sounds evidenced in patients affected by COVID-19 pneumonia can be automatically detected as well by the same class of algorithms. In particular the software VECTOR, suitably devised for interstitial lung diseases, has been employed to process the lung sounds of 28 patient recorded in the emergency room at the university hospital of Modena (Italy) during December 2020. The performance of VECTOR has been compared with diagnostic techniques based on imaging, namely lung ultrasound, chest X-ray and high resolution computed tomography, which have been assumed as ground truth. The results have evidenced a surprising overall diagnostic accuracy of 75% even if the staff of the emergency room has not been suitably trained for lung auscultation and the parameters of the software have not been optimized to detect interstitial pneumonia. These results pave the way to a new approach for monitoring the pulmonary implication in pauci-symptomatic COVID-19 patients.

Clinical and electrocardiographic characteristics at admission of COVID-19/SARS-CoV2 pneumonia infection

BACKGROUND

The aim of the present study was to compare clinical and electrocardiographic characteristics of patients with COVID-19 pneumonia in Modena, Emilia Romagna, Italy.

METHODS

Patients admitted to the emergency department for suspected COVID-19 pneumonia from March the 16th to April the 15th were enrolled in the study. COVID-19 pneumonia was confirmed by positive nasopharyngeal swab. Primary endpoint was 30-day mortality.

RESULTS

201 patients were diagnosed with COVID-19 pneumonia. Compared to survivors, patients who died were older (79.7 ± 10.8 vs 65.6 ± 14.1, p < 0.001), with a more complex cardiovascular history, including coronary artery disease (CAD, 33.3% vs 13.3%, p = 0.004), atrial fibrillation (23.8 vs 8.8, p = 0.011) and chronic kidney disease (CKD 35.7% vs 7.0%, p < 0.001). 30-day mortality was 20,9% in these patients; atrial fibrillation (OR 12.74, 95% CI 3.65-44.48, p < 0.001), ST-segment depression (OR 5.30, 95% CI 1.50-18.81, p = 0.010) and QTc-interval prolongation (OR 3.17, 95% CI 1.24-8.10, p = 0.016) at ECG admission were associated to an increased mortality risk. On the contrary, sinus rhythm (OR 0.08, 95% CI 0.02-0.27, p < 0.001) and low-molecular weight heparin (LMWH) administration (OR 0.08, 95% CI 0.02-0.29, p < 0.001) were related to reduced mortality. At multivariate analysis, after adjustment for age, sex, diabetes, CAD, and MCA admission, sinus rhythm (HR 2.7, CI 95% 1.1-7.0, p = 0.038) and LMWH (HR 8.5, 95% CI 2.0-36.6, p = 0.004) were confirmed to be independent predictors of increased survival.

CONCLUSION

Sinus rhythm at ECG admission in COVID-19 pneumonia patients was associated with greater survival as well as LMWH administration, which conferred an overall better outcome.

Flaxseed dietary fibers suppress postprandial lipemia and appetite sensation in young men

BACKGROUND AND AIM

Dietary fibers (DF) are linked to a reduced risk of life-style diseases, which relate to their physiological effects in the gastrointestinal tract. The aim was to examine whether flaxseed DF-enriched meals suppress postprandial lipemia and reduce appetite.

METHODS AND RESULTS

Four different iso-caloric meals were tested in 18 young men in a double-blind randomized crossover design. Test meals were served after an overnight fast. DF content and source were: control (C): 1.4 g/MJ; whole flaxseed (WF): 2.4 g/MJ from whole flaxseeds; low-mucilage dose (LM): 2.4 g/MJ from flaxseed DF; high-mucilage dose (HM): 3.4 g/MJ from flaxseed DF. During the 7 h test day, subjective appetite sensation was assessed using visual analogue scales and appetite-regulating hormones, and lipemia and glycemia were measured, after which ad libitum energy intake was recorded. There was a significant time × meal effect on triacylglycerols (TG) (p = 0.02) and an 18% smaller area under the curve (AUC) for TG after meal HM compared to meal C was observed (p < 0.01). AUC for insulin was smaller after both LM and HM meals compared to C and WF meals. Higher mean ratings of satiety (p < 0.01) and fullness (p = 0.03) was seen following the HM meal compared to meal C. AUC for ghrelin, CCK and GLP-1 and ad libitum energy intake did not differ between meals, but ghrelin response exhibited a different response pattern after the mucilage-containing meals.

CONCLUSION

These findings suggest that flaxseed DF may suppress postprandial lipemia and appetite although subsequent energy intake was not affected.

icoshift: A versatile tool for the rapid alignment of 1D NMR spectra

The increasing scientific and industrial interest towards metabonomics takes advantage from the high qualitative and quantitative information level of nuclear magnetic resonance (NMR) spectroscopy. However, several chemical and physical factors can affect the absolute and the relative position of an NMR signal and it is not always possible or desirable to eliminate these effects a priori. To remove misalignment of NMR signals a posteriori, several algorithms have been proposed in the literature. The icoshift program presented here is an open source and highly efficient program designed for solving signal alignment problems in metabonomic NMR data analysis. The icoshift algorithm is based on correlation shifting of spectral intervals and employs an FFT engine that aligns all spectra simultaneously. The algorithm is demonstrated to be faster than similar methods found in the literature making full-resolution alignment of large datasets feasible and thus avoiding down-sampling steps such as binning. The algorithm uses missing values as a filling alternative in order to avoid spectral artifacts at the segment boundaries. The algorithm is made open source and the Matlab code including documentation can be downloaded from www.models.life.ku.dk.