Surgical resection of lumbar intradural metastatic renal cell carcinoma.

A 60-year-old male with renal cell carcinoma (RCC) presented with back pain, weakness, and bowel and bladder urgency. MRI demonstrated a cauda equina tumor at L2. Following L1-3 laminectomies, intraoperative ultrasound localized the tumor. After dural opening, a vascular tumor was adherent to the cauda equina. Intraoperative nerve stimulation helped to identify the nerve rootlets. Tumor was removed in a piecemeal fashion. Tumor dissection caused periodic spasms in L1-3 distributions. A neuromonitoring checklist was used to recover motor evoked potential signals with elevated mean arterial pressures. Hemostasis was challenging with the vascular tumor. Intraoperative ultrasound confirmed tumor debulking. Pathology confirmed metastatic RCC.

Transpeduncular Approach for Resection of Ruptured Fourth Ventricular Arteriovenous Malformation.

The lateral recess of the fourth ventricle can be challenging to access surgically. We present a case of a previously ruptured arteriovenous malformation (AVM) of the fourth ventricle that was surgically resected via a retrosigmoid craniotomy and transinferior cerebellar peduncular approach. The patient is a 54-year-old female who experienced an intraventricular hemorrhage several months before presentation to our institution. Imaging studies suggested the presence of an AVM located in the right lateral recess of the fourth ventricle. After discussion of all possible treatment options, the patient elected to proceed with surgical resection. To access the lesion, we performed a retrosigmoid craniotomy and entered into the fourth ventricle via s small incision in the inferior cerebellar peduncle. Postoperative imaging demonstrated complete removal of the AVM nidus. Video 1 demonstrates this approach and discusses the anatomic landmarks used to guide resection.

Accuracy of a pre-trained sentiment analysis (SA) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 Albanian earthquake.

Traditionally, earthquake impact assessments have been made via fieldwork by non-governmental organisations (NGO's) sponsored data collection; however, this approach is time-consuming, expensive and often limited. Recently, social media (SM) has become a valuable tool for quickly collecting large amounts of first-hand data after a disaster and shows great potential for decision-making. Nevertheless, extracting meaningful information from SM is an ongoing area of research. This paper tests the accuracy of the pre-trained sentiment analysis (SA) model developed by the no-code machine learning platform MonkeyLearn using the text data related to the emergency response and early recovery phase of the three major earthquakes that struck Albania on the 26th November 2019. These events caused 51 deaths, 3000 injuries and extensive damage. We obtained 695 tweets with the hashtags: #Albania #AlbanianEarthquake, and #albanianearthquake from the 26th November 2019 to the 3rd February 2020. We used these data to test the accuracy of the pre-trained SA classification model developed by MonkeyLearn to identify polarity in text data. This test explores the feasibility to automate the classification process to extract meaningful information from text data from SM in real-time in the future. We tested the no-code machine learning platform's performance using a confusion matrix. We obtained an overall accuracy (ACC) of 63% and a misclassification rate of 37%. We conclude that the ACC of the unsupervised classification is sufficient for a preliminary assessment, but further research is needed to determine if the accuracy is improved by customising the training model of the machine learning platform.

Large-Scale Molecular Dynamics Simulations of Cellular Compartments.

Molecular dynamics or MD simulation is gradually maturing into a tool for constructing in vivo models of living cells in atomistic details. The feasibility of such models is bolstered by integrating the simulations with data from microscopic, tomographic and spectroscopic experiments on exascale supercomputers, facilitated by the use of deep learning technologies. Over time, MD simulation has evolved from tens of thousands of atoms to over 100 million atoms comprising an entire cell organelle, a photosynthetic chromatophore vesicle from a purple bacterium. In this chapter, we present a step-by-step outline for preparing, executing and analyzing such large-scale MD simulations of biological systems that are essential to life processes. All scripts are provided via GitHub.

A computational chemistry perspective on the current status and future direction of hepatitis B antiviral drug discovery.

Computational chemical biology, applied to research on hepatitis B virus (HBV), has two major branches: bioinformatics (statistical models) and first-principle methods (molecular physics). While bioinformatics focuses on statistical tools and biological databases, molecular physics uses mathematics and chemical theory to study the interactions of biomolecules. Three computational techniques most commonly used in HBV research are homology modeling, molecular docking, and molecular dynamics. Homology modeling is a computational simulation to predict protein structure and has been used to construct conformers of the viral polymerase (reverse transcriptase domain and RNase H domain) and the HBV X protein. Molecular docking is used to predict the most likely orientation of a ligand when it is bound to a protein, as well as determining an energy score of the docked conformation. Molecular dynamics is a simulation that analyzes biomolecule motions and determines conformation and stability patterns. All of these modeling techniques have aided in the understanding of resistance mutations on HBV non-nucleos(t)ide reverse-transcriptase inhibitor binding. Finally, bioinformatics can be used to study the DNA and RNA protein sequences of viruses to both analyze drug resistance and to genotype the viral genomes. Overall, with these techniques, and others, computational chemical biology is becoming more and more necessary in hepatitis B research. This article forms part of a symposium in Antiviral Research on "An unfinished story: from the discovery of the Australia antigen to the development of new curative therapies for hepatitis B."

QMachine: commodity supercomputing in web browsers.

BACKGROUND: Ongoing advancements in cloud computing provide novel opportunities in scientific computing, especially for distributed workflows. Modern web browsers can now be used as high-performance workstations for querying, processing, and visualizing genomics' "Big Data" from sources like The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) without local software installation or configuration. The design of QMachine (QM) was driven by the opportunity to use this pervasive computing model in the context of the Web of Linked Data in Biomedicine. RESULTS: QM is an open-sourced, publicly available web service that acts as a messaging system for posting tasks and retrieving results over HTTP. The illustrative application described here distributes the analyses of 20 Streptococcus pneumoniae genomes for shared suffixes. Because all analytical and data retrieval tasks are executed by volunteer machines, few server resources are required. Any modern web browser can submit those tasks and/or volunteer to execute them without installing any extra plugins or programs. A client library provides high-level distribution templates including MapReduce. This stark departure from the current reliance on expensive server hardware running "download and install" software has already gathered substantial community interest, as QM received more than 2.2 million API calls from 87 countries in 12 months. CONCLUSIONS: QM was found adequate to deliver the sort of scalable bioinformatics solutions that computation- and data-intensive workflows require. Paradoxically, the sandboxed execution of code by web browsers was also found to enable them, as compute nodes, to address critical privacy concerns that characterize biomedical environments.

ImageJS: Personalized, participated, pervasive, and reproducible image bioinformatics in the web browser.

BACKGROUND: Image bioinformatics infrastructure typically relies on a combination of server-side high-performance computing and client desktop applications tailored for graphic rendering. On the server side, matrix manipulation environments are often used as the back-end where deployment of specialized analytical workflows takes place. However, neither the server-side nor the client-side desktop solution, by themselves or combined, is conducive to the emergence of open, collaborative, computational ecosystems for image analysis that are both self-sustained and user driven. MATERIALS AND METHODS: ImageJS was developed as a browser-based webApp, untethered from a server-side backend, by making use of recent advances in the modern web browser such as a very efficient compiler, high-end graphical rendering capabilities, and I/O tailored for code migration. RESULTS: Multiple versioned code hosting services were used to develop distinct ImageJS modules to illustrate its amenability to collaborative deployment without compromise of reproducibility or provenance. The illustrative examples include modules for image segmentation, feature extraction, and filtering. The deployment of image analysis by code migration is in sharp contrast with the more conventional, heavier, and less safe reliance on data transfer. Accordingly, code and data are loaded into the browser by exactly the same script tag loading mechanism, which offers a number of interesting applications that would be hard to attain with more conventional platforms, such as NIH's popular ImageJ application. CONCLUSIONS: The modern web browser was found to be advantageous for image bioinformatics in both the research and clinical environments. This conclusion reflects advantages in deployment scalability and analysis reproducibility, as well as the critical ability to deliver advanced computational statistical procedures machines where access to sensitive data is controlled, that is, without local "download and installation".

Cost-utility analysis in spine care: a systematic review.

BACKGROUND CONTEXT: Despite the importance of the information provided by cost-utility analyses (CUAs), there has been a lack of these types of studies performed in the area of spinal care. PURPOSE: To systematically review cost-utility studies published on spinal care between 1976 and 2010. STUDY DESIGN: Systematic review. METHODS: All CUAs pertaining to spinal care published between 1976 and 2010 were identified using the cost-effectiveness analysis (CEA) registry database (Tufts Medical Center, Institute for Clinical Research and Health Policy) and National Health Service Economic Evaluation Database (NHS EED). The keywords used to search both the registry databases were the following: spine, spinal, neck, back, cervical, lumbar, thoracic, and scoliosis. Search of the CEA registry provided a total of 28 articles, and the NHS EED yielded an additional 5, all of which were included in this review. Each article was reviewed for the study subject, methodology, and results. Data contained within the databases for each of the 33 articles were recorded, and the manuscripts were reviewed to provide insight into the funding source, analysis perspective, discount rate, and cost-utility ratios. RESULTS: There was wide variation among the 33 studies in methodology. There were 17 operative, 13 nonoperative, and 3 imaging studies. Study subjects included lumbar spine (n=27), cervical spine (n=4), scoliosis (n=1), and lumbar and cervical spine (n=1). Twenty-three of the studies were based on the clinical data from prospective randomized studies, 7 on decision models, 2 on prospective observational data, and 1 on a retrospective case series. Sixty cost-utility ratios were reported in the 33 articles. Of the ratios, 19 of 60 (31.6%) were cost saving, 27 of 60 (45%) were less than $100,000/quality-adjusted life year (QALY) gain, and 14 of 60 (23.3%) were greater than $100,000/QALY gain. Only four of 33 (12%) studies contained the four key criteria of cost-effectiveness research recommended by the US Panel on Cost-Effectiveness in Health and Medicine. CONCLUSIONS: Thirty-three CUA studies and 60 cost-utility ratios have been published on various aspects of spinal care over the last 30 years. Certain aspects of spinal care have been shown to be cost effective. Further efforts, however, are needed to better define the value of many aspects of spinal care. Future CUA studies should consider societal cost perspective and carefully consider the durability of clinical benefit in determining a study time horizon.