Cultivability of<i>Streptococcus thermophilus</i>in Grana Padano cheese whey starters

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Single-isocenter multiple-target stereotactic radiosurgery for multiple brain metastases: dosimetric evaluation of two automated treatment planning systems

Purpose Automated treatment planning systems are available for linear accelerator (linac)-based single-isocenter multi-target (SIMT) stereotactic radiosurgery (SRS) of brain metastases. In this study, we compared plan quality between Brainlab Elements Multiple Brain Metastases (Elements MBM) software which utilizes dynamic conformal arc therapy (DCAT) and Varian HyperArc (HA) software using a volumetric modulated arc therapy (VMAT) technique. Patients and methods Between July 2018 and April 2021, 36 consecutive patients &gt;= 18 years old with 367 metastases who received SIMT SRS at UPMC Hillman Cancer San Pietro Hospital, Rome, were retrospectively evaluated. SRS plans were created using the commercial software Elements MBM SRS (Version 1.5 and 2.0). Median cumulative gross tumor volume (GTV) and planning tumor volume (PTV) were 1.33 cm(3) and 3.42 cm(3), respectively. All patients were replanned using HA automated software. Extracted dosimetric parameters included mean dose (D-mean) to the healthy brain, volumes of the healthy brain receiving more than 5, 8,10, and 12 Gy (V-5Gy, V-8Gy, V-10Gy and V-12Gy), and doses to hippocampi. Results Both techniques resulted in high-quality treatment plans, although Element MBM DCAT plans performed significantly better than HA VMAT plans, especially in cases of more than 10 lesions). Median V-12Gy was 13.6 (range, 1.87-45.9) cm(3) for DCAT plans and 18.5 (2.2-62,3) cm(3) for VMAT plans (p &lt; 0.0001), respectively. Similarly, V-10Gy, V-8Gy, V-5Gy (p &lt; 0.0001) and median dose to the normal brain (p = 0.0001) were favorable for DCAT plans. Conclusions Both Elements MBM and HA systems were able to generate high-quality plans in patients with up to 25 brain metastases. DCAT plans performed better in terms of normal brain sparing, especially in patients with more than ten lesions and limited total tumor volume

The Microbiota of Grana Padano Cheese. A Review

Grana Padano (GP) is the most appreciated and marketed cheese with Protected Designation of Origin in the world. The use of raw milk, the addition of undefined cultures (defined as ‘sieroinnesto naturale’), the peculiar manufacturing proces, and the long ripening make the cheese microbiota play a decisive role in defining the quality and the organoleptic properties of the product. The knowledge on the microbial diversity associated with GP has been the subject, in recent years, of several studies aimed at understanding its composition and characteristics in order, on the one hand, to improve its technological performances and, on the other hand, to indirectly enhance the nutritional quality of the product. This review aims to briefly illustrate the main available knowledge on the composition and properties of the GP microbiota, inferred from dozens of studies carried out by both classical microbiology techniques and metagenomic analysis. The paper will essentially, but not exclusively, be focused on the lactic acid bacteria (LAB) derived from starter (SLAB) and the non-starter bacteria, both lactic (NSLAB) and non-lactic, of milk origin

Detection and identification of Lactobacillus helveticus bacteriophages by PCR

A PCR protocol for detection of Lactobacillus helveticus bacteriophages was optimized. PCR was designed taking into account the sequence of the lys gene of temperate bacteriophage W-0303 and optimized to obtain a fragment of 222 bp using different Lb. helveticus phages from our collection. PCR was applied to total phage DNA extracted from 53 natural whey starters used for the production of Grana cheese and all gave the expected fragment. The presence of actively growing phages in the cultures was verified by traditional tests. Several PCR products of the lys gene were sequenced and aligned. The resulting sequences showed variable heterogeneity between the phages.Fil: Zago, Miriam. Centro di Ricerca per le Produzioni Foraggere e Lattiero Casearie; ItaliaFil: Rossetti, Lia. Centro di Ricerca per le Produzioni Foraggere e Lattiero Casearie; ItaliaFil: Reinheimer, Jorge Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Lactología Industrial. Universidad Nacional del Litoral. Facultad de Ingeniería Química. Instituto de Lactología Industrial; ArgentinaFil: Carminati, Domenico. Centro di Ricerca per le Produzioni Foraggere e Lattiero Casearie; ItaliaFil: Giraffa, Giorgio. Centro di Ricerca per le Produzioni Foraggere e Lattiero Casearie; Itali

Bacterial Community of Grana Padano PDO Cheese and Generical Hard Cheeses: DNA Metabarcoding and DNA Metafingerprinting Analysis to Assess Similarities and Differences

The microbiota of Protected Designation of Origin (PDO) cheeses plays an essential role in defining their quality and typicity and could be applied to protect these products from counterfeiting. To study the possible role of cheese microbiota in distinguishing Grana Padano (GP) cheese from generical hard cheeses (HC), the microbial structure of 119 GP cheese samples was studied by DNA metabarcoding and DNA metafingerprinting and compared with 49 samples of generical hard cheeses taken from retail. DNA metabarcoding highlighted the presence, as dominant taxa, of Lacticaseibacillus rhamnosus, Lactobacillus helveticus, Streptococcus thermophilus, Limosilactobacillus fermentum, Lactobacillus delbrueckii, Lactobacillus spp., and Lactococcus spp. in both GP cheese and HC. Differential multivariate statistical analysis of metataxonomic and metafingerprinting data highlighted significant differences in the Shannon index, bacterial composition, and species abundance within both dominant and subdominant taxa between the two cheese groups. A supervised Neural Network (NN) classification tool, trained by metagenotypic data, was implemented, allowing to correctly classify GP cheese and HC samples. Further implementation and validation to increase the robustness and improve the predictive capacity of the NN classifier will be needed. Nonetheless, the proposed tool opens interesting perspectives in helping protection and valorization of GP and other PDO cheeses