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The antimicrobial activity of a methanolic extract of amurca (olive oil lees) was determined against both Gram-positive (L. monocytogenes and S. aureus) and Gram-negative (E. coli D157:H7 and S. enteritidis) foodborne pathogens at 10 °C or 37 °C using microdilution and disk diffusion methods, and its relative activity was compared to selected antibiotics. Minimum inhibitory (MIC) and minimum bactericidal (MBC) concentrations of amurca extract ranged from 60 to 80 µl/ml at 37 °C after 24 h against all tested strains. At 10 °C, amurca was more inhibitory with MIC and MBC values of 40 and 60 µl/ml, respectively, after 7 d against tested strains. Amurca at 40 µl/ml reduced numbers of tested pathogens by 2.5 to 3.2 log 10 CFU/ml at 10 °C after 7 d, but was not inhibitory at 37 °C after 24 h. Protein prepared from amurca was not antimicrobial. The relative antimicrobial activity (inhibition zone ratio) of 80 µl/ml amurca methanolic extract compared to chloramphenicol, erythromycin, gentamycin and tetracycline ranged from 0.36 to 1.0 against Gram-negative and from 0.45 to 2.0 against Gram-positive bacteria. In addition, amurca extract inhibited E. coli D157:H7 02-0628 and S. aureus 26127 which were resistant to tetracycline and chloramphenicol, respectively.

Background: Maternal depression and anxiety are significant public health concerns that play an important role in the health and well-being of mothers and children. The COVID-19 pandemic, the consequential lockdowns and related safety restrictions worldwide negatively affected the mental health of pregnant and postpartum women. Methods: This regional study aimed to develop a machine learning (ML) model for the prediction of maternal depression and anxiety. The study used a dataset collected from five Arab countries during the COVID-19 pandemic between July to December 2020. The population sample included 3569 women (1939 pregnant and 1630 postpartum) from five countries (Jordan, Palestine, Lebanon, Saudi Arabia, and Bahrain). The performance of seven machine learning algorithms was assessed for the prediction of depression and anxiety symptoms. Results: The Gradient Boosting (GB) and Random Forest (RF) models outperformed other studied ML algorithms with accuracy values of 83.3% and 83.2% for depression, respectively, and values of 82.9% and 81.3% for anxiety, respectively. The Mathew’s Correlation Coefficient was evaluated for the ML models; the Naïve Bayes (NB) and GB models presented the highest performance measures (0.63 and 0.59) for depression and (0.74 and 0.73) for anxiety, respectively. The features’ importance ranking was evaluated, the results showed that stress during pregnancy, family support, financial issues, income, and social support were the most significant values in predicting anxiety and depression. Conclusion: Overall, the study evidenced the power of ML models in predicting maternal depression and anxiety and proved to be an efficient tool for identifying and predicting the associated risk factors that influence maternal mental health. The deployment of machine learning models for screening and early detection of depression and anxiety among pregnant and postpartum women might facilitate the development of health prevention and intervention programs that will enhance maternal and child health in low- and middle-income countries.

Objective: Maternal nutrition is considered an important pillar in the pregnancy outcomes for both mother and infant. A mother’s malnutrition and inadequate nutrient intake is associated with many undesirable pregnancy outcomes. Hence, assessing the nutritional status of the mother in the early stages of the pregnancy and preventing any inadequacy can preclude many health problems for both mother and infant. Therefore, this study aimed to assess the adequacy of nutrient intakes among Jordanian pregnant women as compared to their corresponding dietary reference intakes (DRIs). Methods: This cross-sectional study was conducted at a major University Hospital in Jordan. Three hundred pregnant women were invited to participate in the study and 286 agreed to participate. Fifty pregnant women were enrolled at week 9, then 96 pregnant women were at week 20 and 137 pregnant women were at week 30 of pregnancy. The participants completed the interview-based demographic questionnaire, pregnancy physical activity questionnaire, and quantitative food frequency questionnaire (FFQ). Results: The mean energy intake was 2768.9 ± 767.8 kcal/day and it was significantly higher in the 3rd trimester (p < 0.05). Women in the 3rd trimester consumed significantly more protein, carbohydrates, and sugar than women in the 1st and 2nd trimesters (p < 0.05). The pregnant women in the 3rd trimester consumed more sodium than women in the 1st and 2nd trimesters (p < 0.05). The vitamin K intake was significantly (p = 0.045) lower in the 2nd trimester than the 1st and 3rd trimesters. The calcium intake was significantly higher in the 3rd trimester than the 1st and 2nd trimesters (p = 0.021). The total micronutrient (vitamins B1, B2, B3, B6, B12, and D, calcium, and iron) intakes derived from dietary supplements and food sources throughout the 3 trimesters was significantly higher in the 3rd trimester than the 1st and 2nd trimesters (p < 0.05). The vitamin D, calcium, and iron intakes had the most significant increases between the 1st and 3rd trimesters (p < 0.001), while folic acid intake was significantly higher in the 1st trimester than the 2nd and 3rd trimester (p < 0.001). Most women exceeded the tolerable upper intake level (UL) for sodium in all trimesters, while 82% of women exceeded the UL of folic acid in the 1st trimester and from the supplement, not the diet. Conclusion: While the intake of some nutrients from food alone remains below the DRIs in the diets of pregnant women, the intake of other nutrients is above the UL. Raising the awareness of pregnant women about their diet and how a supplement intake can reduce the risk of inadequate intake for many micronutrients and improve their pregnancy outcomes is of great importance.

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