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Background During pregnancy, dynamic changes occur not only in coagulation, but also in immune responses. We report a case of a patient who developed an inflammatory disease in the central nervous system during late pregnancy and was challenging to diagnose. Case presentation A 27-year-old woman experienced dysarthria during pregnancy at approximately 33 gestational weeks. From the 38th week of pregnancy, she developed general fatigue, clumsiness of the left hand and numbness of the right side of the body. After delivery of her child at 41 weeks' gestation, she was moved to the neurology ward for further investigation. She showed limb ataxia of the left extremities and sensory disturbance on the right side of the body, including the face. Magnetic resonance imaging detected multiple gadolinium-enhanced lesions at the brainstem and cerebrum. High-dose intravenous methylprednisolone pulse therapy was effective, but her symptoms still remained, including palatal myoclonus. Diagnosing this patient was challenging, but clinically isolated syndrome or multiple sclerosis and neuro-Behҫet's disease were the main candidates.Conclusions We present a case of a pregnant woman who developed brain inflammatory lesions with unknown etiology. Longitudinal follow up is mandatory for diagnosis with careful tapering of oral prednisolone.

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Fluorescence imaging is widely used for the mesoscopic mapping of neuronal connectivity. However, neurite reconstruction is challenging, especially when neurons are densely labelled. Here we report a strategy for the fully automated reconstruction of densely labelled neuronal circuits. Firstly, we established stochastic super-multicolour labelling with up to seven different fluorescent proteins using the Tetbow method. With this method, each neuron was labelled with a unique combination of fluorescent proteins, which were then imaged and separated by linear unmixing. We also established an automated neurite reconstruction pipeline based on the quantitative analysis of multiple dyes (QDyeFinder). To classify colour combinations, we used a newly developed unsupervised clustering algorithm, dCrawler, in which data points in multi-dimensional space were clustered based on a given threshold distance. Our new strategy allows for the reconstruction of neurites for up to hundreds of neurons at a millimetre scale without manual tracing.