Described is the synthesis of two biotinylated derivatives of a cytotoxic macrocycle. Pull-down assays indicate that this macrocycle targets the N-middle domain of Hsp90. Untagged compound can effectively compete away tagged compound-Hsp90 protein complexes, confirming the binding specificity of the macrocycle for Hsp90. The macrocycle is similar in potency to other structurally-related analogs of Sansalvamide A (San A) and induces apoptosis via a caspase 3 mechanism. Unlike other San A derivatives, we show that the macrocycle does not inhibit binding between C-terminal client proteins and co-chaperones and Hsp90, suggesting that it has a unique mechanism of action.

Bow Hunter’s syndrome (BHS) is a rare cause of vertebrobasilar insufficiency and is reported to most commonly be caused by vertebral artery impingement on cervical vertebrae osteophytes. We report a case in a 56-year-old male patient who on investigation of recurrent posterior circulation ischaemic strokes was found to have BHS. The aetiology of the syndrome in this patient is due to a particularly unusual aberrancy in the path of the atlantoaxial portion of the culprit left vertebral artery. Aberrancy of the distal portion of the vertebral artery is in itself a rare entity, and there are few reports of it in relation to BHS. The patient in this case was successfully treated with endovascular sacrifice of the vertebral artery with no further dynamic occlusive symptoms.

Purpose To train deep learning convolutional neural network (CNN) models for classification of clinically significant Chiari malformation type I (CM1) on MRI to assist clinicians in diagnosis and decision making. Methods A retrospective MRI dataset of patients diagnosed with CM1 and healthy individuals with normal brain MRIs from the period January 2010 to May 2020 was used to train ResNet50 and VGG19 CNN models to automatically classify images as CM1 or normal. A total of 101 patients diagnosed with CM1 requiring surgery and 111 patients with normal brain MRIs were included (median age 30 with an interquartile range of 23–43; 81 women with CM1). Isotropic volume transformation, image cropping, skull stripping, and data augmentation were employed to optimize model accuracy. K-fold cross validation was used to calculate sensitivity, specificity, and the area under receiver operating characteristic curve (AUC) for model evaluation. Results The VGG19 model with data augmentation achieved a sensitivity of 97.1% and a specificity of 97.4% with an AUC of 0.99. The ResNet50 model achieved a sensitivity of 94.0% and a specificity of 94.4% with an AUC of 0.98. Conclusions VGG19 and ResNet50 CNN models can be trained to automatically detect clinically significant CM1 on MRI with a high sensitivity and specificity. These models have the potential to be developed into clinical support tools in diagnosing CM1.