7 research outputs found
Gut Microbiota Profile of Infants with Breastfeeding and Mixed Feeding Patterns
We explore the gut microbiota profiles of 103 stool samples collected from infants at the age of 4 and 6 months in Jakarta, Indonesia. We performed 16S rRNA gene sequencing with Illumina MiSeq to identify the diversity, structure, and composition of the gut microbiota from those stool samples. Among 103 stool samples, 55 and 48 samples were collected from infants with breastfeeding and mixed feeding patterns, respectively. We found that the most abundant bacteria were Bifidobacteriales from the phylum of Actinobacteria (43.05%), Lactobacillales from the phylum of Firmicutes (28.39%), and Enterobacterales from the phylum of Proteobacteria (13.75%). The alpha and beta diversity analysis showed that the association between feeding patterns and differences in the microbial communities was not statistically significant (p-value >0.05). Our study did not show a difference in the gut microbiota pattern between the two feeding pattern groups. This result contributed to the variety of the world gut microbiota profile data in infants
Diagnosis among 230 meningitis suspects based on clinical characteristics and CSF microscopy and culture.
<p>Diagnosis among 230 meningitis suspects based on clinical characteristics and CSF microscopy and culture.</p
CSF and clinical characteristics of 166 HIV-negative patients with suspected TB-meningitis according to culture and PCR-result.
<p>Three culture negative/PCR positive patients were not included because microscopy was positive. PMN = polymorphonuclear cell, MN = mononuclear cell, IQR = interquartile range.</p
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Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats.
In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development's (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security