Elevated cobalt levels in metal-on-polyethylene knee megaprostheses: a prospective 1-year cohort study of 56 patients with hip and knee megaprostheses.

BACKGROUND AND PURPOSE: Concerns have emerged regarding elevated levels of cobalt and chromium in patients with metal-on-metal megaprostheses. This prospective study aims to identify systemic cobalt and chromium levels in metal-on-polyethylene knee and hip megaprostheses and their associations with other factors. METHODS: 56 patients underwent knee or hip megaprosthesis surgery at 2 sarcoma centers. Serum cobalt and chromium levels were measured preoperatively and thrice within the first year using inductively coupled plasma mass spectrometry. RESULTS: A statistically significant difference in serum cobalt levels (1.4 ppb; 95% confidence interval [CI] 0.0-3.3) was observed 1 year after knee megaprosthesis surgery compared with preoperative levels. In contrast no difference in chromium levels was observed after 1 year compared with preoperative levels (0.05 ppb; CI 0.0-0.8). An association between younger age, higher eGFR, and increased cobalt levels was observed. No significant correlations were found between ion levels and resection length or the number of modular connections. CONCLUSION: We found elevated serum ion levels in metal-on-polyethylene knee megaprostheses in contrast to metal-on-polyethylene hip megaprostheses. Furthermore, a positive correlation between cobalt and chromium levels, and between cobalt and eGFR was identified, along with a negative correlation between cobalt and age. This study highlights the importance of monitoring systemic cobalt and chromium levels in patients with megaprostheses.

Deciphering the crystal structure of a novel nanobody against the NEIL1 DNA glycosylase.

Nanobodies (VHHs) are single-domain antibodies with three antigenic CDR regions and are used in diverse scientific applications. Here, an ∼14 kDa nanobody (A5) specific for the endonuclease VIII (Nei)-like 1 or NEIL1 DNA glycosylase involved in the first step of the base-excision repair pathway was crystallized and its structure was determined to 2.1 Šresolution. The crystals posed challenges due to potential twinning and anisotropic diffraction. Despite inconclusive twinning indicators, reprocessing in an orthorhombic setting and molecular replacement in space group P21212 enabled the successful modeling of 96% of residues in the asymmetric unit, with final Rwork and Rfree values of 0.199 and 0.229, respectively.

RNA oligomers at atomic resolution containing 1-methylpseudouridine, an essential building block of mRNA vaccines.

All widely used mRNA vaccines against COVID-19 contain in their sequence 1-methylpseudouridine (m1Ψ) instead of uridine. In this publication, we report two high resolution crystal structures (at up to 1.01 and 1.32 Å, respectively) of one such double-stranded 12-mer RNA sequence crystallized in two crystal forms. The structures are compared with similar structures which do not contain this modification. Additionally, the X-ray structure of 1-methyl-pseudouridine itself was determined.

Errors in structural biology are not the exception.

During the COVID-19 pandemic, the structural biology community swung into action quickly and efficiently, and many urgent questions were solved by macromolecular structure determination. The Coronavirus Structural Task Force evaluated all structures from SARS-CoV-1 and SARS-CoV-2, but errors in measurement, data processing and modelling are present beyond these structures and throughout the structures deposited in the Protein Data Bank. Identifying them is only the first step; in order to minimize the impact that errors have in structural biology, error culture needs to change. It should be emphasized that the atomic model which is published is an interpretation of the measurement. Furthermore, risks should be minimized by addressing issues early and by investigating the source of a given problem, so that it may be avoided in the future. If we as a community can do this, it will greatly benefit experimental structural biologists as well as downstream users who are using structural models to deduce new biological and medical answers in the future.

The impact of AlphaFold2 on experimental structure solution.

AlphaFold2 is a machine-learning based program that predicts a protein structure based on the amino acid sequence. In this article, we report on the current usages of this new tool and give examples from our work in the Coronavirus Structural Task Force. With its unprecedented accuracy, it can be utilized for the design of expression constructs, de novo protein design and the interpretation of Cryo-EM data with an atomic model. However, these methods are limited by their training data and are of limited use to predict conformational variability and fold flexibility; they also lack co-factors, post-translational modifications and multimeric complexes with oligonucleotides. They also are not always perfect in terms of chemical geometry. Nevertheless, machine learning-based fold prediction is a game changer for structural bioinformatics and experimentalists alike, with exciting developments ahead.

Effect of negative pressure wound therapy after surgical removal of deep-seated high-malignant soft tissue sarcomas of the extremities and trunk wall-study protocol for a randomized controlled trial.

BACKGROUND: Sarcomas are a heterogeneous group of rare malignant tumors of mesenchymal origin in the musculoskeletal system. The main treatment is surgery often supplemented with pre-or postoperative radiotherapy. A retrospective study by Bedi et al. indicated that negative pressure wound therapy (NPWT) reduced the risk of postoperative wound complications in patients treated with preoperative radiation followed by surgical tumor removal of lower extremity soft tissue sarcomas (STS), and the use of NPWT was not associated with an increased risk of local recurrence. Previous studies have shown that NPWT can reduce postoperative complications. STS surgeries are a high-risk procedure concerning wound complications. METHODS: Non-blinded single-center randomized controlled trial comparing NPWT versus conventional wound dressing and postoperative wound complications after surgical removal of deep-seated high-malignant STS of the extremities or trunk wall Sample-size calculation: 154 STS patients (80% risk of avoiding type II error, 5% risk of type I error, and an 80% wound complication risk) Block randomization of 8 into: Group A: Conventional wound dressing Group B: NPWT (PREVENA PLUS™ Incision Management System) Inclusion criteria: Surgery for a deep-seated STS of an extremity or the trunk wall Exclusion criteria: Age < 18 years, plastic surgery, low malignant/borderline STS, chemotherapy, preoperative radiotherapy, allergic/hypersensitive to acrylic adhesives or silver, unwilling/unable to provide informed consent, metastatic disease, and ischemic surgeries Primary study endpoints were set as major wound complications defined by O'Sullivan et al. as a secondary surgery under anesthesia for wound repairs and wound management without secondary surgery within 4 months postoperatively. Secondary study endpoints among others are Musculoskeletal Tumor Society Score (MSTS), Toronto Extremity Salvage Score (TESS), and European Quality of Life - 5 Dimensions (EQ-5D). Approval from the Scientific Ethical Committee and the Data Protection Agency has been obtained, and the study is registered at clinicaltrial.gov . This study did not apply for external funding. DISCUSSION: Many new medical devices and technical solutions are currently being introduced, and even though some documentation regarding the use of NPWT, e.g., in joint replacement surgery exist, it is also important to seek documentation for this treatment principle in STS surgery. TRIAL REGISTRATION: Registered at ClinicalTrials.gov NCT04960332 and approved on 11 July 2021.

Artificial intelligence in the experimental determination and prediction of macromolecular structures.

Machine learning methods, in particular convolutional neural networks, have been applied to a variety of problems in cryo-EM and macromolecular crystallographic structure solution. However, they still have only limited acceptance by the community, mainly in areas where they replace repetitive work and allow for easy visual checking, such as particle picking, crystal centering or crystal recognition. With Artificial Intelligence (AI) based protein fold prediction currently revolutionizing the field, it is clear that their scope could be much wider. However, whether we will be able to exploit this potential fully will depend on the manner in which we use machine learning: training data must be well-formulated, methods need to utilize appropriate architectures, and outputs must be critically assessed, which may even require explaining AI decisions.

Detecting ice artefacts in processed macromolecular diffraction data with machine learning.

Contamination with diffraction from ice crystals can negatively affect, or even impede, macromolecular structure determination, and therefore detecting the resulting artefacts in diffraction data is crucial. However, once the data have been processed it can be very difficult to automatically recognize this problem. To address this, a set of convolutional neural networks named Helcaraxe has been developed which can detect ice-diffraction artefacts in processed diffraction data from macromolecular crystals. The networks outperform previous algorithms and will be available as part of the AUSPEX web server and the CCP4-distributed software.

3DBionotes COVID-19 edition.

SUMMARY: The web platform 3DBionotes-WS integrates multiple web services and an interactive web viewer to provide a unified environment in which biological annotations can be analyzed in their structural context. Since the COVID-19 outbreak, new structural data from many viral proteins have been provided at a very fast pace. This effort includes many cryogenic electron microscopy (cryo-EM) studies, together with more traditional ones (X-rays, NMR), using several modeling approaches and complemented with structural predictions. At the same time, a plethora of new genomics and interactomics information (including fragment screening and structure-based virtual screening efforts) have been made available from different servers. In this context, we have developed 3DBionotes-COVID-19 as an answer to: (i) the need to explore multiomics data in a unified context with a special focus on structural information and (ii) the drive to incorporate quality measurements, especially in the form of advanced validation metrics for cryo-EM. AVAILABILITY AND IMPLEMENTATION: https://3dbionotes.cnb.csic.es/ws/covid19. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Making the invisible enemy visible.

During the COVID-19 pandemic, structural biologists rushed to solve the structures of the 28 proteins encoded by the SARS-CoV-2 genome in order to understand the viral life cycle and enable structure-based drug design. In addition to the 204 previously solved structures from SARS-CoV-1, 548 structures covering 16 of the SARS-CoV-2 viral proteins have been released in a span of only 6 months. These structural models serve as the basis for research to understand how the virus hijacks human cells, for structure-based drug design, and to aid in the development of vaccines. However, errors often occur in even the most careful structure determination - and may be even more common among these structures, which were solved quickly and under immense pressure. The Coronavirus Structural Task Force has responded to this challenge by rapidly categorizing, evaluating and reviewing all of these experimental protein structures in order to help downstream users and original authors. In addition, the Task Force provided improved models for key structures online, which have been used by Folding@Home, OpenPandemics, the EU JEDI COVID-19 challenge and others.

Haruspex: A Neural Network for the Automatic Identification of Oligonucleotides and Protein Secondary Structure in Cryo-Electron Microscopy Maps.

In recent years, three-dimensional density maps reconstructed from single particle images obtained by electron cryo-microscopy (cryo-EM) have reached unprecedented resolution. However, map interpretation can be challenging, in particular if the constituting structures require de-novo model building or are very mobile. Herein, we demonstrate the potential of convolutional neural networks for the annotation of cryo-EM maps: our network Haruspex has been trained on a carefully curated set of 293 experimentally derived reconstruction maps to automatically annotate RNA/DNA as well as protein secondary structure elements. It can be straightforwardly applied to newly reconstructed maps in order to support domain placement or as a starting point for main-chain placement. Due to its high recall and precision rates of 95.1 % and 80.3 %, respectively, on an independent test set of 122 maps, it can also be used for validation during model building. The trained network will be available as part of the CCP-EM suite.

Survival and prognostic factors at time of diagnosis in high-grade appendicular osteosarcoma: a 21 year single institution evaluation from east Denmark.

BACKGROUND: Survival of patients with high-grade osteosarcoma (HOS), the most common primary bone cancer, has not improved significantly the last 30 years and the disease remains a major challenge. The purpose of this study is to evaluate survival in relation to prognostic factors at time of diagnosis among patients diagnosed with primary appendicular HOS in East Denmark between 1990 and 2010. MATERIAL AND METHODS: 101 patients (median age = 20 years, female/male ratio = 56/45) diagnosed with primary appendicular high-grade osteosarcoma between 1990 and 2010 were included in this study. Initially, 156 patients diagnosed with osteosarcoma between 1990 and 2010 were identified through the population based Regional Database of Pathology, which covers a population of approximately 2.7 million (east Denmark). 55 patients were excluded due to (A) tumor was low grade (n = 22), (B) located in axial skeleton (n = 18), (C) incorrect diagnosis (n = 11) or (D) biopsy represented a tumor relapse from a former primary osteosarcoma (n = 4). Overall survival was evaluated using the Kaplan-Meier survival analysis and log-rank test. Prognostic factors were analyzed using uni- and multivariate cox-regression method with variables scored equally in the model. p Values <.05 were considered statistically significant. RESULTS: The probability of 5- and 10-year survival was 51% (95% CI: 41-61) and 46% (95% CI: 36-56), respectively. Metastatic stage at diagnosis and tumor size ≥10 cm measured radiologically at the largest diameter were independent prognostic factors for decreased survival with significant increased hazard-risks of 3.5 (95% CI: 1.9-6.5) and 1.97 (95% CI: 1.1-3.6), respectively. DISCUSSION: In this single institution evaluation of primary appendicular HOS we found 5-and 10-year survival rates consistent with international standards for this patient group. Distant metastases and tumor size ≥10 cm at the time of diagnosis were independent prognostic factors for decreased survival in our cohort. These results underline the importance of awareness and early referral from the primary sector.

Background modelling of diffraction data in the presence of ice rings.

An algorithm for modelling the background for each Bragg reflection in a series of X-ray diffraction images containing Debye-Scherrer diffraction from ice in the sample is presented. The method involves the use of a global background model which is generated from the complete X-ray diffraction data set. Fitting of this model to the background pixels is then performed for each reflection independently. The algorithm uses a static background model that does not vary over the course of the scan. The greatest improvement can be expected for data where ice rings are present throughout the data set and the local background shape at the size of a spot on the detector does not exhibit large time-dependent variation. However, the algorithm has been applied to data sets whose background showed large pixel variations (variance/mean > 2) and has been shown to improve the results of processing for these data sets. It is shown that the use of a simple flat-background model as in traditional integration programs causes systematic bias in the background determination at ice-ring resolutions, resulting in an overestimation of reflection intensities at the peaks of the ice rings and an underestimation of reflection intensities either side of the ice ring. The new global background-model algorithm presented here corrects for this bias, resulting in a noticeable improvement in R factors following refinement.