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Leptospirosis, caused by the pathogenic leptospires, is one of the most widespread zoonotic diseases known. Leptospirosis cases can occur either sporadically or in epidemics, Humans are susceptible to infection by a variety of serovars. These bacteria are antigenically diverse. Changes in the antigenic composition of lipopolysaccharide (LPS) are thought to account for this antigenic diversity. The presence of more than 200 recognized antigenic types (termed serovars) of pathogenic leptospires have complicated our understanding of this genus. Definitive diagnosis is suggested by isolation of the organism by culture or a positive result on the microscopic agglutination test (MAT). Only specialized laboratories perform serologic tests; hence, the decision to treat should not be delayed while waiting for the test results.

The present study aimed to evaluate the bacteriological quality of beef (n = 52), lamb (n = 52) and beef offal (n = 52) marketed in Casablanca, Morocco. Meat and offal samples (n = 156), were collected randomly from butcheries, supermarkets, and slaughterhouses. Two sampling periods were considered, one during the hot season and the second one during the cold season. The samples were analyzed for the presence of the following bacteria: Escherichia coli, coagulase-positive Staphylococcus, Clostridium perfringens, Salmonella, and Listeria monocytogenes. Results indicated that counts of the aerobic plate count, and fecal coliforms were particularly high in all the samples analyzed. E. coli, coagulase-positive Staphylococcus and C. perfringens were detected in 37.8, 16, and 4.5% of the meat samples, respectively. Neither Salmonella nor L. monocytogenes were isolated from meat samples. Approximately 26.9% of beef, 34.6% of lamb and 28.8% of beef offal samples contained bacteria above the maximum limits established by the Moroccan regulatory standards for meat and meat products. Seasonality and the distribution location significantly (p < 0.05) affected bacterial populations: the hot season and butcheries appeared to be cases where the highest populations of bacteria in meat were observed. These high levels of microbiological contamination attest the poor hygienic quality of meat and offal, possibly due to uncontrolled processing, storage, and handling of these products.

Background and Aim: Campylobacteriosis is a common foodborne disease epidemiologically linked to the consumption of poultry products. However, other sources, such as raw or contaminated milk, contaminated water or ice, contact with infected livestock, and pets, are reported. This study aimed to evaluate the prevalence and resistance to microbial resistance of Campylobacter coliM in broiler farms in the region of Marrakesh Safi, Morocco. Materials and Methods: The study was conducted between May and December 2017 and involved 35 broiler farms. One hundred and five cloacal swabs were collected from the eight provinces in the region of Marrakesh Safi, Morocco. Bacteriology method NM ISO/TS 10272-3: 2013 was used to isolate and identify Campylobacter spp. Molecular identification (polymerase chain reaction) was used for confirmation. A disk diffusion method on Mueller-Hinton agar was used for susceptibility testing. Five antibiotic agents, including first-line drugs, were evaluated. Results: Among 105 samples, 71.4% (75/105) were positive for Campylobacter spp. test and 56% (42/75) of isolates belonged to the species coli. Susceptibility profiles showed that 95.2% of C. coli strains were resistant to ampicillin, 92.8% to erythromycin and tetracycline, 85.7% to ciprofloxacin, and 7.1% to gentamicin. Conclusion: This study underlines the need to strengthen implementation of specific control procedures to decrease contamination of poultry meat with Campylobacter spp. and to reduce the use of antibiotics in the poultry sector.

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BACKGROUND Hospitals have been identified as very high-risk places for COVID-19 transmission between health care workers and patients who do not have COVID-19. Health care workers are the most at-risk population to contract and transmit the infection, especially to already vulnerable patients who do not have COVID-19. In low-income countries, routine testing is not feasible due to the high cost of testing; therefore, presenting the risk of uncontrolled transmission within non–COVID-19 treatment wards. This challenge necessitated the development of an affordable intermediary screening tool that would enable early identification of potentially infected health care workers and for early real time DNA–polymerase chain reaction testing prioritization. This would limit the contact time of potentially infected health care workers with the patients but also enable efficient use of the limited testing kits. OBJECTIVE The aims of this study are to describe an early warning in-hospital mobile risk analysis app for screening COVID-19 and to determine the feasibility and user-friendliness of the app among health care workers. METHODS The primary result of this research project was the development of a mobile-based daily early warning system for in-hospital transmission of COVID-19. Overall, the Early Warning System for In-Hospital Transmission of COVID-19 (EWAS) mobile app was found to be feasible, with over 69% of the health care workers having logged more than 67% of the required times. Over 93% of the participants reported that the tool was easy to use. RESULTS The primary result of this research project was the development of a mobile-based daily early warning system for in-hospital transmission of COVID-19. Overall, the Early Warning System for In-Hospital Transmission of COVID-19 (EWAS) mobile app was found to be feasible, with 69% of the health care workers (69/100) having logged more than 67% of the required times. Of the 100 participants, 93 reported that the tool was easy to use. CONCLUSIONS The EWAS mobile app is a feasible and user-friendly daily risk scoring tool for preventing in-hospital transmission of COVID-19. Although it was not designed to be a diagnostic tool but rather a screening tool, there is a need to evaluate its sensitivity in predicting persons likely to have contracted COVID-19.