Four-Year Monitoring of Foodborne Pathogens in Raw Milk Sold by Vending Machines in Italy

Prevalence data were collected from official microbiological records monitoring four selected foodborne pathogens (Salmonella spp., Listeria monocytogenes, Escherichia coli O157:H7 and Campylobacter jejuni) in raw milk sold by self-service vending machines in seven Italian Regions (n. 60907 samples from 1239 vending machines) during the years 2008 to 2011. Data of samples analyzed both by culture-based and real-time PCR methods were collected in one Region. A total of 100 raw milk consumers in four regions were interviewed while purchasing raw milk from vending machines. One hundred and seventy eight samples out of 60907 were positive for one of the four foodborne pathogens investigated; overall, 18 samples were positive for Salmonella spp., 83 for L. monocytogenes, 24 for E. coli O157:H7 and 53 for C. jejuni in the seven Regions investigated. There were no significant differences in prevalence among Regions, but a significant increase in C. jejuni prevalence was observed over the years. A comparison of the two different analysis methods showed that real-time PCR is from 2.71 to 9.40 times more sensitive than culture-based method. Data on consumer habits showed that some behaviors may enhance the risk of infection due to raw milk consumption: 37% of consumers do not boil milk before consumption, 93% never use an insulated bag to transport raw milk home, and raw milk is consumed by children under five years of age. The study emphasizes that end-product controls alone are not sufficient to guarantee an adequate level of consumer protection. The beta distribution of positive samples in this study and the data on raw milk consumer habits are useful and appropriate for the development of a National Quantitative Risk Assessment of Salmonella spp., L. monocytogenes, E. coli O157 and C. jejuni related to raw milk consumption

[Atrial natriuretic factor in patients with cardiac decompensation before and after chronic therapy with an angiotensin-converting enzyme inhibitor].

BACKGROUND: Patients with severe congestive heart failure often have high plasma Atrial Natriuretic Factor (ANF) and neurohormonal activation. Ace inhibitors give clinical and hemodynamic benefits and lower plasma angiotensin and norepinephrine levels. The interactions between ANF and the Ace inhibitors are mainly modulated via the renin angiotensin system. METHODS: Plasma ANF, renin activity, urinary aldosterone and catecholamine levels were evaluated in 10 patients with congestive heart failure (at baseline, after 15 days of adequate treatment with digoxin and diuretics, and after 45 days of enalapril) in order to assess the changes of ANF and vasoconstrictor neurohormones with chronic Ace inhibitor therapy. RESULTS: ANF increased significantly in the congestive heart failure group compared to a normal subject control group (P < 0.001). After digoxin and diuretic therapy NHYA class improved significantly, but no significant hormonal changes were found. On the contrary, the addition of enalapril caused a significant decrease of plasma ANF and urinary aldosterone and catecholamines (P < 0.05). CONCLUSIONS: The relationship between the renin angiotensin system and catecholamines is complex but our findings indicate that: 1) Traditional therapy is effective in improving symptoms, but cannot induce a decrease of vasoconstrictive neurohormones; 2) ACE inhibitor therapy reduces ANF and neurohormonal activation. 3) ANF is a useful marker in evaluating the response to treatment

Sand Flies and Pathogens in the Lowlands of Emilia-Romagna (Northern Italy)

Cases of sand fly-borne diseases in the Emilia-Romagna region, such as meningitis caused by Toscana virus and human leishmaniasis, are reported annually through dedicated surveillance systems. Sand flies are abundant in the hilly part of the region, while the lowland is unsuitable habitat for sand flies, which are found in lower numbers in this environment with respect to the hilly areas. In this study, we retrieved sand flies collected during entomological surveillance of the West Nile virus (from 2018 to 2021) to assess their abundance and screen them for the presence of pathogens. Over the four-year period, we collected 3022 sand flies, more than half in 2021. The most abundant sand fly species was Phlebotomus (Ph.) perfiliewi, followed by Ph. perniciosus; while more rarely sampled species were Ph. papatasi, Ph. mascittii and Sergentomyia minuta. Sand flies were collected from the end of May to the end of September. The pattern of distribution of the species is characterized by an abundant number of Ph. perfiliewi in the eastern part of the region, which then falls to almost none in the western part of the region, while Ph. perniciosus seems more uniformly distributed throughout. We tested more than 1500 female sand flies in 54 pools to detect phleboviruses and Leishmania species using different PCR protocols. Toscana virus and Leishmania infantum, both human pathogens, were detected in 5 pools and 7 pools, respectively. We also detected Fermo virus, a phlebovirus uncharacterized in terms of relevance to public health, in 4 pools. We recorded different sand fly abundance in different seasons in Emilia-Romagna. During the season more favorable for sand flies, we also detected pathogens transmitted by these insects. This finding implies a health risk linked to sand fly-borne pathogens in the surveyed area in lowland, despite being considered a less suitable habitat for sand flies with respect to the hilly areas

Geographical distribution of human, canine and sand fly <i>Leishmania</i>-positive samples, 2013–2017, Emilia-Romagna region (northeastern Italy).

<p>Colors depict the subpopulations inferred by STRUCTURE analysis: yellow for subPopA1; orange for subPopA2; green for subPopB1; brown for subPopB2; light blue for subPopB3; white for samples not submitted to cluster analysis. More than one sample per area are shown by numbers inside the icon. Map generated with Quantum-GIS (<a href="https://www.qgis.org/it/site/" target="_blank">https://www.qgis.org/it/site/</a>).</p

Bayesian phylogenetic tree (cladogram) obtained by the Sainudiin model [29] using data of 14 coincident microsatellite loci for the 62 <i>L</i>. <i>infantum</i> strains from Italy, 49 MLMT profiles available in literature and 3 WHO reference strains.

<p>Posterior probabilities > 0.75 are showed near the nodes. Strains representing the different microsatellite profiles are listed in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006595#pntd.0006595.s003" target="_blank">S1</a> and <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006595#pntd.0006595.s004" target="_blank">S2</a> Tables. Strains designations specify, respectively, the laboratory code, the zymodeme (ND, not defined), the geographical origin (IT, Italy; ER, Emilia-Romagna; FR, France; SP, Spain; GR, Greece; PT, Portugal; DZ, Algeria; TN, Tunisia; KE, Kenia; SD, Sudan; ET, Ethiopia; IN, India; NP, Nepal). Italian samples are differentiated by the color of STRUCTURE designation (K = 2): PopA samples in red, PopB samples in blue.</p

Population genetic characterization of the populations and subpopulations found for 52 <i>L</i>. <i>infantum</i> strains from the Emilia-Romagna region (northeastern Italy).

<p>Population genetic characterization of the populations and subpopulations found for 52 <i>L</i>. <i>infantum</i> strains from the Emilia-Romagna region (northeastern Italy).</p

Differentiation measures (F_st), probability (<i>p</i>-value) and migration rate (Nm) between populations/subpopulations of <i>L</i>. <i>infantum</i> strains (n = 52) from the Emilia-Romagna region (northeastern Italy) as assumed by STRUCTURE.

<p>Differentiation measures (F_st), probability (<i>p</i>-value) and migration rate (Nm) between populations/subpopulations of <i>L</i>. <i>infantum</i> strains (n = 52) from the Emilia-Romagna region (northeastern Italy) as assumed by STRUCTURE.</p

Spatial distribution of 52 <i>L</i>. <i>infantum</i> strains from the Emilia-Romagna region (northeastern Italy) by factorial correspondence analysis (FCA).

<p>Each square represents one microsatellite profile. The designations PopA (red circle), PopB (blue circle), subPopA1 (yellow circle), subPopA2 (orange circle), subPopB1 (green circle), subPopB2 (brown circle) and subPopB3 (light blue circle) correspond to the populations and subpopulations defined by STRUCTURE analysis as shown in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006595#pntd.0006595.g002" target="_blank">Fig 2</a>.</p