OBJECTIVEThe goal of this study was to explore the feasibility and accuracy of using a wearable mixed-reality holographic computer to guide external ventricular drain (EVD) insertion and thus improve on the accuracy of the classic freehand insertion method for EVD insertion. The authors also sought to provide a clinically applicable workflow demonstration.METHODSPre- and postoperative CT scanning were performed routinely by the authors for every patient who needed EVD insertion. Hologram-guided EVD placement was prospectively applied in 15 patients between August and November 2017. During surgical planning, model reconstruction and trajectory calculation for each patient were completed using preoperative CT. By wearing a Microsoft HoloLens, the neurosurgeon was able to visualize the preoperative CT-generated holograms of the surgical plan and perform EVD placement by keeping the catheter aligned with the holographic trajectory. Fifteen patients who had undergone classic freehand EVD insertion were retrospectively included as controls. The feasibility and accuracy of the hologram-guided technique were evaluated by comparing the time required, number of passes, and target deviation for hologram-guided EVD placement with those for classic freehand EVD insertion.RESULTSSurgical planning and hologram visualization were performed in all 15 cases in which EVD insertion involved holographic guidance. No adverse events related to the hologram-guided procedures were observed. The mean ± SD additional time before the surgical part of the procedure began was 40.20 ± 10.74 minutes. The average number of passes was 1.07 ± 0.258 in the holographic guidance group, compared with 2.33 ± 0.98 in the control group (p < 0.01). The mean target deviation was 4.34 ± 1.63 mm in the holographic guidance group and 11.26 ± 4.83 mm in the control group (p < 0.01).CONCLUSIONSThis study demonstrates the use of a head-mounted mixed-reality holographic computer to successfully perform hologram-assisted bedside EVD insertion. A full set of clinically applicable workflow images is presented to show how medical imaging data can be used by the neurosurgeon to visualize patient-specific holograms that can intuitively guide hands-on operation. The authors also provide preliminary confirmation of the feasibility and accuracy of this hologram-guided EVD insertion technique.

Histopathology images are crucial to the study of complex diseases such as cancer. The histologic characteristics of nuclei play a key role in disease diagnosis, prognosis and analysis. In this work, we propose a sparse Convolutional Autoencoder (CAE) for fully unsupervised, simultaneous nucleus detection and feature extraction in histopathology tissue images. Our CAE detects and encodes nuclei in image patches in tissue images into sparse feature maps that encode both the location and appearance of nuclei. Our CAE is the first unsupervised detection network for computer vision applications. The pretrained nucleus detection and feature extraction modules in our CAE can be finetuned for supervised learning in an end-to-end fashion. We evaluate our method on four datasets and reduce the errors of state-of-the-art methods up to 42%. We are able to achieve comparable performance with only 5% of the fullysupervised annotation cost.

Aims and objectives To explore the incidence of delirium in cerebrovascular patients admitted in our Neurosurgery Intensive Care Unit and analyse the risk factors leading to delirium. Background Delirium is one of the most common mental disorders in general hospitals, but the incidence of different kinds of diseases and studies varies. Cerebrovascular patients in our Neurosurgery Intensive Care Unit are high‐risk groups for delirium; identifying risk factors for delirium and taking early interventions are crucial for patient prognosis. Design A prospective study. Methods A convenience sampling method was used to collect data from 128 patients in the Neurosurgery Intensive Care Unit of Xuanwu Hospital, Capital Medical University, Beijing, China, between May 2016–January 2017. Researchers used Confusion Assessment Method for the Intensive Care Unit (Chinese version) to assess each patient's delirium statement twice a day at regular times. We also collected other independent data variables and followed up the short‐term clinical outcomes daily. Results On the basis of Confusion Assessment Method for the Intensive Care Unit evaluation, patients were divided into a delirium group and a nondelirium group. The prevalence of delirium among the 128 patients was 42.2%. Multivariate analysis showed that severity of illness, fever, the use of physical restraints and sleep deprivation were independent predictors of delirium in cerebrovascular patients in the Neurosurgery Intensive Care Unit. Conclusions Cerebrovascular patients in the Neurosurgery Intensive Care Unit with a critical condition, fever or use of physical restraints or experiencing sleep deprivation were more prone to delirium. Relevance to clinical practice Cerebrovascular patients in the Neurosurgery Intensive Care Unit showed a high incidence of delirium. There are many risk factors leading to delirium, some of which are independent predictors of intensive care delirium. Patients with delirium will suffer various adverse effects upon their short‐term clinical outcomes. Therefore, nurses should pay close attention to changes in a patient's mental state and learn about the risk factors associated with delirium, in order to be able to take early measures to prevent delirium.

Background: CD44, a multifunctional receptor, undergoes cleavage to produce an intracytoplasmic domain (CD44-ICD) that translocates into the nucleus. Results: CD44-ICD binds to a novel DNA consensus sequence and activates many genes. Conclusion:We finally explain the multifunctionality of CD44 and reveal new genes affected by CD44. Significance: Our findings will accelerate the understanding of how CD44-ICD regulates a multitude of cell functions.

The increasing number of applications of three-dimensional (3D) tumor spheroids as an in vitro model for drug discovery requires their adaptation to large-scale screening formats in every step of a drug screen, including large-scale image analysis. Currently there is no readyto-use and free image analysis software to meet this large-scale format. Most existing methods involve manually drawing the length and width of the imaged 3D spheroids, which is a tedious and time-consuming process. This study presents a high-throughput image analysis software application -SpheroidSizer, which measures the major and minor axial length of the imaged 3D tumor spheroids automatically and accurately; calculates the volume of each individual 3D tumor spheroid; then outputs the results in two different forms in spreadsheets for easy manipulations in the subsequent data analysis. The main advantage of this software is its powerful image analysis application that is adapted for large numbers of images. It provides high-throughput computation and quality-control workflow. The estimated time to process 1,000 images is about 15 min on a minimally configured laptop, or around 1 min on a multi-core performance workstation. The graphical user interface (GUI) is also designed for easy quality control, and users can manually override the computer results. The key method used in this software is adapted from the active contour algorithm, also known as Snakes, which is especially suitable for images with uneven illumination and noisy background that often plagues automated imaging processing in high-throughput screens. The complimentary "Manual Initialize" and "Hand Draw" tools provide the flexibility to SpheroidSizer in dealing with various types of spheroids and diverse quality images. This high-throughput image analysis software remarkably reduces labor and speeds up the analysis process. Implementing this software is beneficial for 3D tumor spheroids to become a routine in vitro model for drug screens in industry and academia.