FIRST RESULTS ON THE PRESENCE AND THE MOLECULAR CHARACTERIZATION OF ANISAKID NEMATODES IN MARINE FISH CAUGHT OFF NORTHERN SARDINIA

Anisakidosis is a parasitic zoonosis caused by nematodes of the family Anisakidae, belonging to the genera Anisakis, Contracaecum and Pseudoterranova. Molecular studies have shown that Anisakis larvae comprise a number of sibling species, which have different genetic structures, hosts and geographical distribution. A great variety of fish species can harbour infectious third stage larvae of this nematode. The preliminary results of a study carried out to evaluate the occurrence of this parasite in commercial fish caught off northern Sardinia are herein reported. From October 2008 to November 2009, 599 specimens of 8 commercial fish species were examined for anisakid larvae through visual inspection of body cavity and peptic digestion of the muscle. Isolated Anisakis sp. larvae were observed at light microscope and identified as Type I or Type II (sensu Berland, 1961). Out of 599 fish examined, 239 (40%) were infected by 1187 anisakid larvae, belonging to the genera Anisakis (1169 Type I and 18 Type II) and Hysterothylacium (692). The molecular identification of Anisakis spp. was carried out on a subsample of 30% of Type I larvae and all Type II larvae. Specimens were firstly examined using a species-specific PCR, with primers designed for Anisakis pegreffii (APEF) and Anisakis physeteris (APHF), and ITS-2 of nuclear rDNA. The results were confirmed by the analysis of the ITS region of nuclear rDNA (ITS-1, 5.8S and ITS-2) using the restriction enzymes HinfI and HhaI in PCR-RFLP. Type I larvae examined were all identified as A. pegreffii, and Type II were all A. physeteris. This is the first contribution to the epidemiology and molecular characterization of Anisakis spp. in commercial fish caught off Sardinia

Milk cathelicidin and somatic cell counts in dairy goats along the course of lactation

This research communication reports the evaluation of cathelicidin in dairy goat milk for its relationship with the somatic cell count (SCC) and microbial culture results. Considering the limited performances of SCC for mastitis monitoring in goats, there is interest in evaluating alternative diagnostic tools. Cathelicidin is an antimicrobial protein involved in innate immunity of the mammary gland. In this work, half-udder milk was sampled bimonthly from a herd of 37 Alpine goats along an entire lactation and tested with the cathelicidin ELISA together with SCC and bacterial culture. Cathelicidin and SCC showed a strong correlation (r = 0.72; n = 360 milk samples). This was highest in mid-lactation (r = 0.83) and lowest in late lactation (r = 0.61), and was higher in primiparous (0.80, n = 130) than in multiparous goats (0.71, n = 230). Both markers increased with stage of lactation, but cathelicidin increased significantly less than SCC. Inaddition, peak level in late lactation was lower for cathelicidin (5.05-fold increase) than for SCC (7.64-fold increase). Twenty-one (5.8%) samples were positive to bacteriological culture, 20 for coagulase-negative staphylococci and one for Streptococcus spp.; 18 of them were positive to the cathelicidin ELISA (85.71% sensitivity). Sensitivity of SCC >500 000 and of SCC >1 000 000 cells/ml was lower (71.43 and 23.81%, respectively). Therefore, the high correlation of cathelicidin with SCC during the entire lactation, along with its lower increase in late lactation and good sensitivity indetecting intramammary infection (IMI), indicate a potential for monitoring subclinical mastitis in dairy goats. However, based on this preliminary assessment, specificity should be improved (40.41% for cathelicidin vs. 54.57 and 67.85% for SCC >500 000 and >1 000 000 cells/ml, respectively). Therefore, the application of cathelicidin for detecting goat IMI will require further investigation and optimization, especially concerning the definition of diagnostic thresholds

Detection of celery (Apium graveolens) allergen in foods of animal and plant origin by droplet digital PCR assay

Celery is included among the allergenic foods that, under the EU 1169/2011 regulation, must be declared in the ingredient list. However, disposition covers only allergens that are voluntary used as ingredients and not the accidental presence of allergens in a food as consequence of cross contamination. To guarantee compliance with food allergen regulations and protect health of food-allergic consumers are needed specific and sensitive methods to detect the presence of allergens in foods. Detection of allergens relies of protein- and DNA-based methods. Real-time PCR (RT-PCR) targeting sequences from the mannitol dehydrogenase (Mtd) gene is currently the method of choice for detection and quantification of celery in foods. However, quantification by RT-PCR methods needs standard calibration curves of the target DNA. To overcome this limitation in the present study the use of a droplet digital PCR (dd-PCR) assay has been proposed for the quantification of celery in foods. A preliminarily optimization of the dd-PCR protocol was conducted using serial DNA dilution extracted from celery powder. Ideal primer probe concentrations were 0.9 μM of both forward and reverse primers and 0.250 μM of probe. The optimal annealing temperature was at 60 °C. The limit of detection (LOD) was 0.20 ± 0.12 Cp/μL while the limit of quantification (LOQ) was 0.83 ± 0.20 Cp/μL. The dd-PCR assay showed no cross-reactivity with other vegetal species, indicating a good specificity. No effect of food matrix was observed on the dd-PCR performance. The method was able to quantify the presence of celery in commercial foods of animal and plant origin

Modulation of adipose-derived stem cell behavior by prostate pathology-associated plasma: insights from in vitro exposure

Adipose-derived stem cells (ADSCs) are promising in regenerative medicine. Their proliferation, survival and activation are influenced by specific signals within their microenvironment, also known as niche. The stem cell niche is regulated by complex interactions between multiple cell types. When transplanted in a specific area, ADSCs can secrete several immunomodulatory factors. At the same time, a tumor microenvironment can influence stem cell behavior, modulating proliferation and their ability to differentiate into a specific phenotype. Whitin this context, we exposed ADSCs to plasma samples derived from human patients diagnosed with prostate cancer (PC), or precancerous lesions (PL), or benign prostatic hyperplasia (BPH) for 4, 7 or 10 days. We then analyzed the expression of main stemness-related markers and cell-cycle regulators. We also measured cytokine production and polyamine secretion in culture medium and evaluated cell morphology and collagen production by confocal microscopy. The results obtained from this study show significant changes in the morphology of ADSCs exposed to plasma samples, especially in the presence of prostate cancer plasma, suggesting important implications in the use of ADSCs for the development of new treatments and application in regenerative medicine

Preliminary efficacy and safety data of nivolumab in never smoker patients with advanced squamous NSCLC: Experience from Italian sites participating in the Expanded Access Programme (EAP)

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A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra

peer-reviewedLactoferrin (LF) is a glycoprotein naturally present in milk. Its content varies throughout lactation, but also with mastitis; therefore it is a potential additional indicator of udder health beyond somatic cell count. Condequently, there is an interest in quantifying this biomolecule routinely. First prediction equations proposed in the literature to predict the content in milk using milk mid-infrared spectrometry were built using partial least square regression (PLSR) due to the limited size of the data set. Thanks to a large data set, the current study aimed to test 4 different machine learning algorithms using a large data set comprising 6,619 records collected across different herds, breeds, and countries. The first algorithm was a PLSR, as used in past investigations. The second and third algorithms used partial least square (PLS) factors combined with a linear and polynomial support vector regression (PLS + SVR). The fourth algorithm also used PLS factors, but included in an artificial neural network with 1 hidden layer (PLS + ANN). The training and validation sets comprised 5,541 and 836 records, respectively. Even if the calibration prediction performances were the best for PLS + polynomial SVR, their validation prediction performances were the worst. The 3 other algorithms had similar validation performances. Indeed, the validation root mean squared error (RMSE) ranged between 162.17 and 166.75 mg/L of milk. However, the lower standard deviation of cross-validation RMSE and the better normality of the residual distribution observed for PLS + ANN suggest that this modeling was more suitable to predict the LF content in milk from milk mid-infrared spectra (R2v = 0.60 and validation RMSE = 162.17 mg/L of milk). This PLS +ANN model was then applied to almost 6 million spectral records. The predicted LF showed the expected relationships with milk yield, somatic cell score, somatic cell count, and stage of lactation. The model tended to underestimate high LF values (higher than 600 mg/L of milk). However, if the prediction threshold was set to 500 mg/L, 82% of samples from the validation having a content of LF higher than 600 mg/L were detected. Future research should aim to increase the number of those extremely high LF records in the calibration set

MOLECULAR CHARACTERIZATION OF ANISAKID NEMATODES IN FISHES OF NORTHERN SARDINIAN SEA

The authors report results of analysis carried out during 2008-2010 for identification and molecular characterization of larval Anisakis nematodes isolated from fishes of the northern Sardinian sea