We also report here the results of a review of the literature looking at articles describing cases of gallbladder metastasis from breast cancer.Background Livestock diseases impact the livelihoods of pastoralists. Brucellosis, a neglected zoonotic disease is highly prevalent in this system with an estimated 16% of livestock population in sub-Saharan Africa infected with the disease. The objective of this study was to assess knowledge of livestock diseases and the risk of exposure to brucellosis among pastoralists living in Kajiado County of Kenya. Methods The study sites included pastoralist communities living in rural and peri-urban areas within the County. Both primary and secondary data were collected using participatory methods including pairwise ranking, proportional piling and probing and a review of the published literature. Protoporphyrin IX Exposure risk assessment was conducted according to the CODEX Alimentarius framework Hazard identification, hazard characterization, exposure assessment and risk estimation. Results According to pastoralists, livestock diseases that frequently occurred in their flocks and herds were contagious caprine pleuropneumonia, lumpy skin disease and foot and mouth disease; but zoonoses, including anthrax and brucellosis, were also mentioned during focus group discussions. Potential pathways of exposure to brucellosis and other zoonoses included consumption of unpasteurized milk, handling infected aborted materials without protective measures and consumption of raw meat and raw blood. Consumption of unpasteurized milk and handling infected aborted materials without protectives were linked with high risk of exposure to household members living in rural areas, with the risk level within the peri-urban areas ranked very low to low for most of these risk practices. Conclusions The results call for enhanced public education targeting vulnerable groups to mitigate risks of disease spread and other impacts of brucellosis within the affected pastoralist production systems.The abundance of apomixis in tropical plant genera is poorly understood, and this affects the understanding of speciation and evolution. Hanguanaceae is a tropical monogeneric, dioecious plant family. All but two species are solitary herbs with no capability to spread vegetatively. Viable seeds are often produced when males have not been observed. Our aim was to investigate the presence of apomixis in Hanguana. We used reduced representation genomics to study phylogenetics and genetic variability in all populations of Hanguana in Singapore. We measured genome sizes and estimated ploidy levels in 10 species. Almost all taxa tested were genetically uniform (uniclonal) regardless of the extent of their distribution. The distribution of single clones over distinct localities supports our hypothesis of apomictic reproduction. Only one sexually reproducing native species was detected. Triploid and pentaploid states support our hypothesis that the type of apomixis in Hanguana is gametophytic. Population genomics tools offer a quick and cost-effective way of detecting excess clonality and thereby inferring apomixis. In the case of Hanguana, the presence of male plants is a strong indicator of sexual reproduction, whereas genome triplication is indicative of apomictic reproduction.

To effectively evaluate the compliance degree between the electronic medical records of Traditional Chinese Medicine (TCM) hospitals, as well as the information platform, and the related information standards of electronic medical records, a standard compliance testing scheme based on electronic medical records of TCM outpatients is proposed.

This research selected the data of clinical outpatients accumulated in 10 years by the Digital Medicine Institute of Chengdu University of TCM and processed the data through security check and desensitization process. And then 28348 cases of processed electronic medical records of TCM outpatients were inputted into the standard compliance testing platform for assessment. The result was then outputted.

There are 924 cases among the 28348 that can be rated as five-star medical records, 84 cases four-star, 132 cases three-star, 12460 cases two-star, 13488 one-star, and 1260 cases zero-star through the integrity and standardization test.

By the way of assessing the integrity and standardization of data, the standard compliance test algorithm scheme for electronic medical records of TCM outpatients introduced in this paper can solve the problems such as data unavailability caused by ununified codes and incomplete data in the data-sharing process and provides technical support for the construction of data standardization testing in electronic medical records of TCM outpatients.

By the way of assessing the integrity and standardization of data, the standard compliance test algorithm scheme for electronic medical records of TCM outpatients introduced in this paper can solve the problems such as data unavailability caused by ununified codes and incomplete data in the data-sharing process and provides technical support for the construction of data standardization testing in electronic medical records of TCM outpatients.COVID-19 presents an urgent global challenge because of its contagious nature, frequently changing characteristics, and the lack of a vaccine or effective medicines. A model for measuring and preventing the continued spread of COVID-19 is urgently required to provide smart health care services. This requires using advanced intelligent computing such as artificial intelligence, machine learning, deep learning, cognitive computing, cloud computing, fog computing, and edge computing. This paper proposes a model for predicting COVID-19 using the SIR and machine learning for smart health care and the well-being of the citizens of KSA. Knowing the number of susceptible, infected, and recovered cases each day is critical for mathematical modeling to be able to identify the behavioral effects of the pandemic. It forecasts the situation for the upcoming 700 days. The proposed system predicts whether COVID-19 will spread in the population or die out in the long run. Mathematical analysis and simulation results are presented here as a means to forecast the progress of the outbreak and its possible end for three types of scenarios “no actions,” “lockdown,” and “new medicines.