Intestinal parasites that still plague us today were all over Roman Britain. Breakthroughs, discoveries, and DIY tips sent every weekday. The soldiers guarding the Roman Empire's northwestern frontier had a real parasite problem. Scientists analyzing the sewer drains from the Roman fort Vindolanda (near Hadrian's Wall in northern England) found three types of intestinal parasites --roundworm,whipworm, and . The findings published in the journal mark the first time that has been documented in Roman Britain.

They look like creatures from a gruesome sci-fi movie. But these shocking new images show the creepy ringworms and tapeworms that could be living inside your cat or dog. These parasites are mostly microscopic, but their gory detail is revealed in the images, which have been magnified up to 180 times. They show the importance of taking your pet to the vet regularly to get it wormed, a relatively simple treatment usually involving tablets. On rare occasions, both tapeworms and roundworms can infect humans too – the latter known to potentially cause serious damage to children's eyesight.

An artificial intelligence can detect diarrhoea with up to 98 per cent accuracy by analysing the sounds emanating from toilets. This skill could help us track outbreaks of diseases such as cholera. Maia Gatlin at the Georgia Institute of Technology and her colleagues collected 350 recordings of toilet-based sounds from YouTube and sound database Soundsnap – covering standard defecation, diarrhoea, urination and flatulence. The researchers then used 70 per cent of the recordings to train an AI to recognise audible differences between the four types of excretion. Once they confirmed that the AI could consistently do this with another 10 per cent of the data, they tested the AI's performance using the last 20 per cent of the recordings.

Beijing has dismissed as "ridiculous" a Harvard Medical School study of hospital traffic and search engine data that suggested the new coronavirus may already have been spreading in China last August, and scientists said it offered no convincing evidence of when the outbreak began. Chinese Foreign Ministry spokeswoman Hua Chunying, asked about the research at a news briefing on Tuesday, said: "I think it is ridiculous, incredibly ridiculous, to come up with this conclusion based on superficial observations such as traffic volume." The research, which has not been peer-reviewed by other scientists, used satellite imagery of hospital parking lots in Wuhan - where the disease was first identified in late 2019 - and data for symptom-related queries on search engines for things such as "cough" and "diarrhoea". The study's authors said increased hospital traffic and symptom search data in Wuhan preceded the documented start of the coronavirus pandemic in December 2019. "While we cannot confirm if the increased volume was directly related to the new virus, our evidence supports other recent work showing that emergence happened before identification at the Huanan Seafood market (in Wuhan)," they said.

Child mortality from preventable diseases such as pneumonia and diarrhoea in low and middle-income countries remains a serious global challenge. We combine knowledge with available Demographic and Health Survey (DHS) data from India, to construct Bayesian Networks (BNs) and investigate the factors associated with childhood diarrhoea. We make use of freeware tools to learn the graphical structure of the DHS data with score-based, constraint-based, and hybrid structure learning algorithms. We investigate the effect of missing values, sample size, and knowledge-based constraints on each of the structure learning algorithms and assess their accuracy with multiple scoring functions. Weaknesses in the survey methodology and data available, as well as the variability in the BNs generated, mean that is not possible to learn a definitive causal BN from data. However, knowledge-based constraints are found to be useful in reducing the variation in the graphs produced by the different algorithms, and produce graphs which are more reflective of the likely influential relationships in the data. Furthermore, valuable insights are gained into the performance and characteristics of the structure learning algorithms. Two score-based algorithms in particular, TABU and FGES, demonstrate many desirable qualities; a) with sufficient data, they produce a graph which is similar to the reference graph, b) they are relatively insensitive to missing values, and c) behave well with knowledge-based constraints. The results provide a basis for further investigation of the DHS data and for a deeper understanding of the behaviour of the structure learning algorithms when applied to real-world settings.