Antimicrobial Susceptibility and Frequency of and Genes in Isolated from Slaughtered Pigs.

is known as one of the most common foodborne pathogens worldwide. While salmonellosis is usually self-limiting, severe infections may require antimicrobial therapy. However, increasing resistance of to antimicrobials, particularly fluoroquinolones and cephalosporins, is of utmost concern.

PCR-based detection and serovar identification of Salmonella in retail meat collected from wet markets in Metro Manila, Philippines.

This study aimed to detect Salmonella from retail meat collected from nine wet markets in Metro Manila, and identify the subtypes of Salmonella isolates using molecular serotyping assays from previously developed primers. Of the 720 collected meat samples, 57.64% were found to be Salmonella-contaminated.

Quantitative microbial risk assessment of Cryptosporidium in bivalve samples from Manila Bay, Philippines.

In the Philippines, consumption of bivalves is very common due to its year-round availability and cheap price. However, many consume bivalves as lightly-cooked or raw. This might pose health hazards because bivalves are filter-feeders which act as vehicles for transmission of several pathogens such as the protozoan parasite Cryptosporidium, the causative agent of cryptosporidiosis.

Application of quantitative real-time PCR compared to filtration methods for the enumeration of Escherichia coli in surface waters within Vietnam.

Surface water samples in Vietnam were collected from the Saigon River, rural and suburban canals, and urban runoff canals in Ho Chi Minh City, Vietnam, and were processed to enumerate Escherichia coli. Quantification was done through membrane filtration and quantitative real-time polymerase chain reaction (PCR). Mean log colony-forming unit (CFU)/100 ml E.

Fecal contamination in irrigation water and microbial quality of vegetable primary production in urban farms of Metro Manila, Philippines.

Microbial contamination of fresh produce can present a severe risk to public health. By conducting a rigorous survey of irrigation waters, the impacts of fecal contamination on the quality of produce could be assessed. In this study, surface waters were observed to be contaminated with Escherichia coli, Salmonella spp.

Microbiological quality of fresh produce from open air markets and supermarkets in the Philippines.

This study is the first in the Philippines to conduct a comprehensive assessment of the prevalence of bacterial pathogens and somatic phages in retailed fresh produce used in salad preparation, namely, bell pepper, cabbage, carrot, lettuce, and tomato, using culture and molecular methods. Out of 300 samples from open air and supermarkets, 16.7% tested positive for thermotolerant Escherichia coli, 24.

Fatty acid modulation of autoinducer (AI-2) influenced growth and macrophage invasion by Salmonella Typhimurium.

Autoinducer-2 (AI-2) is a small molecule that is involved in bacterial cell-to-cell signaling whose precursor formation is mediated by luxS. A luxS mutant of Salmonella Typhimurium PJ002 (ΔluxS) was grown in glucose-containing M-9 minimal medium supplemented with varying concentrations (1×, 10×, and 100×) of long-chain fatty acids (linoleic acid, oleic acid, palmitic acid, and stearic acid) to study the influence of fatty acids on growth rate and macrophage invasion. Additionally, in vitro synthesized AI-2 was added to this medium to identify the influence of AI-2 on S.

Differential expression of virulence-related genes in A Salmonella enterica serotype typhimurium luxS mutant in response to autoinducer AI-2 and poultry meat-derived AI-2 inhibitor.

Bacterial cells communicate with each other and respond to external stimuli using signal molecules called autoinducers (AIs). Poultry meat contains inhibitors that apparently interfere with AI-2 signaling. Our objective was to understand the expression of Salmonella Typhimurium genes (using spotted microarrays) in response to AI-2 in the presence, and absence, of poultry meat (PM) derived AI-2 inhibitors.

Use of artificial neural networks to accurately identify Cryptosporidium oocyst and Giardia cyst images.

Cryptosporidium parvum and Giardia lamblia are protozoa capable of causing gastrointestinal diseases. Currently, these organisms are identified using immunofluorescent antibody (IFA)-based microscopy, and identification requires trained individuals for final confirmation. Since artificial neural networks (ANN) can provide an automated means of identification, thereby reducing human errors related to misidentification, ANN were developed to identify Cryptosporidium oocyst and Giardia cyst images.

Identification of Cryptosporidium parvum oocysts by an artificial neural network approach.

Microscopic detection of Cryptosporidium parvum oocysts is time-consuming, requires trained analysts, and is frequently subject to significant human errors. Artificial neural networks (ANN) were developed to help identify immunofluorescently labeled C. parvum oocysts.