Current Standard Approach Research Articles

Predictive Value of Serum Cytokeratin 19 Level for the Feasibility of Conserving Ovaries in Endometrial Cancer.

Objective: To determine the predictive value of cytokeratin 19 (CK19) for evaluating the safety of ovarian preservation in patients with endometrial cancer (EC).Methods: Five hundred and seventeen EC patients hospitalized from November 2010 to June 2016 were reviewed retrospectively. Pre-operative tumor biomarkers including CA125, HE4, CK19, and CA19-9 were obtained. Predictive biomarkers associated with ovarian metastasis were selected using univariate and multivariate Logistic regression. The cut-off values were determined by receiver operating characteristic (ROC) curves. Kaplan-Meier method and Cox multivariate regression model was used to perform survival analysis.Results: Among clinical parameters and biomarkers included, age > 65, type II EC, CA125 ≥ 35 u/ml, CK19 > 3.3 ng/ml, and myometrial invasion ≥ 50% depth appeared as significant predictors of the risk of ovarian involvement in univariable logistic analysis. In multivariable analysis, CK19 > 3.3 ng/ml (OR = 11.541, 95%CI: 1.968–67.668, P = 0.007) and Type II EC (OR = 8.336, 95%CI: 1.456–47.722, P = 0.017) were independent risk predictors of ovarian metastasis in pre-menopausal women. In pre-menopausal women with Type I EC (n = 142), CK19 level could satisfactorily predict the risk of ovarian metastasis (AUC = 0.860, 95%CI: 0.792–0.912, P < 0.001), and when the cut-off point was set as 2.45 ng/ml, the negative predictive value and negative likelihood ratio were 99% and 0.19, with the maximum Youden index of 0.598.Conclusions: The present study advocates the necessity of incorporating serum CK19 measurement into the pre-operative evaluation of EC, especially as extension of current standard approach with ovarian preservation counseling.

Detecting Spurious Correlations With Sanity Tests for Artificial Intelligence Guided Radiology Systems.

Artificial intelligence (AI) has been successful at solving numerous problems in machine perception. In radiology, AI systems are rapidly evolving and show progress in guiding treatment decisions, diagnosing, localizing disease on medical images, and improving radiologists' efficiency. A critical component to deploying AI in radiology is to gain confidence in a developed system's efficacy and safety. The current gold standard approach is to conduct an analytical validation of performance on a generalization dataset from one or more institutions, followed by a clinical validation study of the system's efficacy during deployment. Clinical validation studies are time-consuming, and best practices dictate limited re-use of analytical validation data, so it is ideal to know ahead of time if a system is likely to fail analytical or clinical validation. In this paper, we describe a series of sanity tests to identify when a system performs well on development data for the wrong reasons. We illustrate the sanity tests' value by designing a deep learning system to classify pancreatic cancer seen in computed tomography scans.

Efficacy of Immune Checkpoint Inhibitors Alone or in Combination With Chemotherapy in NSCLC Harboring ERBB2 Mutations

IntroductionIn contrast to other driver mutations, no targeted therapies have yet been approved in ERBB2-mutated NSCLC (HER2mu NSCLC). Nevertheless, several compounds have revealed promising early efficacy data, which need to be evaluated in the context of current standard approaches. Although data on the efficacy of immune checkpoint inhibitors (ICIs) in second or subsequent lines of treatment remain limited and conflicting, there are virtually no data on patient outcome under ICI/platinum-doublet combinations in the first-line setting. MethodsWe retrospectively evaluated outcomes of patients with HER2mu NSCLC treated with ICI alone or in combination with chemotherapy within the German National Network Genomic Medicine Lung Cancer consortium by means of overall response rate (ORR), progression-free survival (PFS), and overall survival (OS). ResultsICI either in combination with chemotherapy or as monotherapy was applied as first-line treatment in 27 patients, whereas 34 received single-agent ICI in second or subsequent lines. Patient characteristics were in line with previously published data. In treatment-naive patients receiving ICI in combination with chemotherapy, the ORR, median PFS, and OS rate at 1 year were 52%, 6 months, and 88%, respectively. In second or subsequent lines, ICI monotherapy was associated with an ORR of 16%, a median PFS of 4 months, and a median OS of 10 months. ConclusionsICIs are effective as monotherapy and in combination with platinum-doublet chemotherapy. Therefore, ICI-based treatments may be found as the current standard of care and benchmark for targeted therapies in HER2mu NSCLC.

Direct Immunodetection of Global A-to-I RNA Editing Activity with a Chemiluminescent Bioassay.

Adenosine-to-inosine (A-to-I) editing is a conserved eukaryotic RNA modification that contributes to development, immune response, and overall cellular function. Here, we utilize Endonuclease V (EndoV), which binds specifically to inosine in RNA, to develop an EndoV-linked immunosorbency assay (EndoVLISA) as a rapid, plate-based chemiluminescent method for measuring global A-to-I editing signatures in cellular RNA. We first optimize and validate our assay with chemically synthesized oligonucleotides. We then demonstrate rapid detection of inosine content in treated cell lines, demonstrating equivalent performance against current standard RNA-seq approaches. Lastly, we deploy our EndoVLISA for profiling differential A-to-I RNA editing signatures in normal and diseased human tissue, illustrating the utility of our platform as a diagnostic bioassay. Together, the EndoVLISA method is cost-effective, straightforward, and utilizes common laboratory equipment, offering a highly accessible new approach for studying A-to-I editing. Moreover, the multi-well plate format makes this the first assay amenable for direct high-throughput quantification of A-to-I editing for applications in disease detection and drug development.

Detection and quantitation of host cell proteins in monoclonal antibody drug products using automated sample preparation and data-independent acquisition LC-MS/MS

Ensuring the removal of host cell proteins (HCPs) during downstream processing of recombinant proteins such as monoclonal antibodies (mAbs) remains a challenge. Since residual HCPs might affect product stability or safety, constant monitoring is required to demonstrate their removal to be below the regulatory accepted level of 100 ng/mg. The current standard analytical approach for this procedure is based on ELISA; however, this approach only measures the overall HCP content. Therefore, the use of orthogonal methods, such as liquid chromatography-mass spectrometry (LC-MS), has been established, as it facilitates the quantitation of total HCPs as well as the identification and quantitation of the individual HCPs present. In the present study, a workflow for HCP detection and quantitation using an automated magnetic bead-based sample preparation, in combination with a data-independent acquisition (DIA) LC-MS analysis, was established. Employing the same instrumental setup commonly used for peptide mapping analysis of mAbs allows for its quick and easy implementation into pre-existing workflows, avoiding the need for dedicated instrumentation or personnel. Thereby, quantitation of HCPs over a broad dynamic range was enabled to allow monitoring of problematic HCPs or to track changes upon altered bioprocessing conditions.

A Modified Triangular Osteosynthesis Protocol for the Rod and Pedicle Screw Fixation of Vertical Unstable Sacral Fractures.

The stabilization of vertical unstable sacral fractures has been a complex problem that is a challenge to current standard approaches. Here, we attempted to evaluate a modified technique for sacral fractures. In the modified triangular osteosynthesis technique, we adopted a vertical and transverse fixation with a rod and pedicle screw system to reduce and fix sacral fractures in 28 subjects. The postsurgery effect of this technique was evaluated by physical examination and radiography. In the postoperative day 1, the patients were able to move body position passively from the lateral side to supine and exercise their legs by themselves. After a follow up of 20 months, radiological evaluation showed that fracture fragment reduction was excellent in 18 (64%), good (displacement 5-10 mm) in 8 (29%), and fair (displacement 10-15 mm) in 2 (7%) patients. Three patients with a preoperative perineal neurological impairment had a complete recovery after surgical decompression. All patients had achieved bone union of fractures, and no loss of fracture reduction was detected. The modified procedures offered an easier approach to fix vertical unstable sacral fractures, thereby achieving quicker and stable functionality. This suggests an alternative approach to manage unstable sacral fractures. 4. The stabilization of vertical unstable sacral fractures has been a complex problem that is a challenge to current standard approaches. We attempted to introduce a modified technique for sacral fractures.

De novo neo-hyaline-cartilage from bovine organoids in viscoelastic hydrogels

Regenerative therapies for articular cartilage are currently clinically available. However, they are associated with several drawbacks that require resolution. Optimizing chondrocyte expansion and their assembly, can reduce the time and costs of these therapies and more importantly increase their clinical success. In this study, cartilage organoids were quickly mass produced from bovine chondrocytes with a new suspension expansion protocol. This new approach led to massive cell proliferation, high viability and the self-assembly of organoids. These organoids were composed of collagen type II, type VI, glycosaminoglycans, with Sox9 positive cells, embedded in a pericellular and interterritorial matrix similarly to hyaline cartilage. With the goal of producing large scale tissues, we then encapsulated these organoids into alginate hydrogels with different viscoelastic properties. Elastic hydrogels constrained the growth and fusion of the organoids inhibiting the formation of a tissue. In contrast, viscoelastic hydrogels allowed the growth and fusion of the organoids into a homogenous tissue that was rich in collagen type II and glycosaminoglycans. The encapsulation of organoids to produce in vitro neocartilage also proved to be superior to the conventional method of encapsulating 2D expanded chondrocytes. This study describes a multimodal approach that involves chondrocyte expansion, organoid formation and their assembly into neohyaline-cartilage which proved to be superior to the current standard approaches used in cartilage tissue engineering. Statement of significanceIn this manuscript, we describe a new and simple methodology to quickly mass produce self-assembling cartilage organoids. Due to their matrix content and structure similarities with native cartilage, these organoids on their own have the potential to revolutionize cartilage research and the manner in which we study signaling pathways, disease progression, tissue engineering, drug development, etc. Furthermore, these organoids and their fast mass production were combined with a key relatively ignored hydrogel characteristic, viscoelasticity, to demonstrate their fusion into a neo-tissue. This has the potential to open the door for large scale cartilage regeneration such as for entire joint surfaces.

Collective Synchronous Spiking in a Brain Network of Coupled Nonlinear Oscillators.

A network of propagating nonlinear oscillatory modes (waves) in the human brain is shown to generate collectively synchronized spiking activity (hypersynchronous spiking) when both amplitude and phase coupling between modes are taken into account. The nonlinear behavior of the modes participating in the network are the result of the nonresonant dynamics of weakly evanescent cortical waves that, as shown recently, adhere to an inverse frequency-wave number dispersion relation when propagating through an inhomogeneous anisotropic media characteristic of the brain cortex. This description provides a missing link between simplistic models of synchronization in networks of small amplitude phase coupled oscillators and in networks built with various empirically fitted models of pulse or amplitude coupled spiking neurons. Overall the phase-amplitude coupling mechanism presented in the Letter shows significantly more efficient synchronization compared to current standard approaches and demonstrates an emergence of collective synchronized spiking from subthreshold oscillations that neither phase nor amplitude coupling alone are capable of explaining.

Deep Convolutional Neural Networks for Displacement Estimation in ARFI Imaging.

Ultrasound elasticity imaging in soft tissue with acoustic radiation force requires the estimation of displacements, typically on the order of several microns, from serially acquired raw data A-lines. In this work, we implement a fully convolutional neural network (CNN) for ultrasound displacement estimation. We present a novel method for generating ultrasound training data, in which synthetic 3-D displacement volumes with a combination of randomly seeded ellipsoids are created and used to displace scatterers, from which simulated ultrasonic imaging is performed using Field II. Network performance was tested on these virtual displacement volumes, as well as an experimental ARFI phantom data set and a human in vivo prostate ARFI data set. In the simulated data, the proposed neural network performed comparably to Loupas's algorithm, a conventional phase-based displacement estimation algorithm; the rms error was [Formula: see text] for the CNN and 0.73 [Formula: see text] for Loupas. Similarly, in the phantom data, the contrast-to-noise ratio (CNR) of a stiff inclusion was 2.27 for the CNN-estimated image and 2.21 for the Loupas-estimated image. Applying the trained network to in vivo data enabled the visualization of prostate cancer and prostate anatomy. The proposed training method provided 26 000 training cases, which allowed robust network training. The CNN had a computation time that was comparable to Loupas's algorithm; further refinements to the network architecture may provide an improvement in the computation time. We conclude that deep neural network-based displacement estimation from ultrasonic data is feasible, providing comparable performance with respect to both accuracy and speed compared to current standard time-delay estimation approaches.

How should we use ibrutinib in patients with mantle cell lymphoma?

How should we use ibrutinib in patients with mantle cell lymphoma?

Suitability of Measurement Tools for Assessing the Prevalence of Child Domestic Work: A Rapid Systematic Review.

Child domestic work (CDW) is a hidden form of child labour. Globally, there were an estimated 17.2 million CDWs aged 5–17 in 2012, but there has been little critical analysis of methods and survey instruments used to capture prevalence of CDW. This rapid systematic review identified and critically reviewed the measurement tools used to estimate CDWs in Low- and Middle-Income Countries, following PRISMA guidelines (PROSPERO registration: CRD42019148702). Fourteen studies were included. In nationally representative surveys, CDW prevalence ranged from 17% among 13–24-year-old females in Haiti to 2% of children aged 10–17 in Brazil. Two good quality studies and one good quality measurement tool were identified. CDW prevalence was assessed using occupation-based methods (n = 9/14), household roster (n = 7) and industry methods (n = 4). Six studies combined approaches. Four studies included task-based questions; one study used this method to formally calculate prevalence. The task-based study estimated 30,000 more CDWs compared to other methods. CDWs are probably being undercounted, based on current standard measurement approaches. We recommend use of more sensitive, task-based methods for inclusion in household surveys. The cognitive and pilot testing of newly developed task-based questions is essential to ensure comprehension. In analyses, researchers should consider CDWs who may be disguised as distant or non-relatives.

Next Generation Imaging Techniques to Define Immune Topographies in Solid Tumors.

In recent years, cancer immunotherapy experienced remarkable developments and it is nowadays considered a promising therapeutic frontier against many types of cancer, especially hematological malignancies. However, in most types of solid tumors, immunotherapy efficacy is modest, partly because of the limited accessibility of lymphocytes to the tumor core. This immune exclusion is mediated by a variety of physical, functional and dynamic barriers, which play a role in shaping the immune infiltrate in the tumor microenvironment. At present there is no unified and integrated understanding about the role played by different postulated models of immune exclusion in human solid tumors. Systematically mapping immune landscapes or “topographies” in cancers of different histology is of pivotal importance to characterize spatial and temporal distribution of lymphocytes in the tumor microenvironment, providing insights into mechanisms of immune exclusion. Spatially mapping immune cells also provides quantitative information, which could be informative in clinical settings, for example for the discovery of new biomarkers that could guide the design of patient-specific immunotherapies. In this review, we aim to summarize current standard and next generation approaches to define Cancer Immune Topographies based on published studies and propose future perspectives.

Optimized Liquid and Gas Phase Fractionation Increases HLA-Peptidome Coverage for Primary Cell and Tissue Samples

MS is the most effective method to directly identify peptides presented on human leukocyte antigen (HLA) molecules. However, current standard approaches often use 500 million or more cells as input to achieve high coverage of the immunopeptidome, and therefore, these methods are not compatible with the often limited amounts of tissue available from clinical tumor samples. Here, we evaluated microscaled basic reversed-phase fractionation to separate HLA peptide samples offline followed by ion mobility coupled to LC–MS/MS for analysis. The combination of these two separation methods enabled identification of 20% to 50% more peptides compared with samples analyzed without either prior fractionation or use of ion mobility alone. We demonstrate coverage of HLA immunopeptidomes with up to 8107 distinct peptides starting with as few as 100 million cells. The increased sensitivity obtained using our methods can provide data useful to improve HLA-binding prediction algorithms as well as to enable detection of clinically relevant epitopes such as neoantigens.

The Influence of the Choice of a Gaussian Filter on the Determination of Areal Surface Texture Parameters

The areal Gaussian filtering is the current standardized approach for the separation of roughness and waviness components of a surface topography for areal surface measurements and analysis. Although the technical specifications (ISO 25178) recommend а certain nesting index for areal Gaussian filters, the use of standard values is not possible in some cases, particularly when the recommended value exceeds the range of the measurement software. Therefore, it is necessary to find an appropriate nesting index value other than that recommended by the standard. The aim of this study is to investigate the influence of Gaussian filter selection on areal surface texture parameter determination for structured surfaces. The results suggest that the nesting index of a Gaussian filter for areal surface parameter determination and analysis should be at least three times the scale of the coarsest surface structure.

Application of machine learning classifiers for microcomputed tomography data assessment of mouse bone microarchitecture

The current standard approach for analyzing cortical bone structure and trabecular bone microarchitecture from micro-computed tomography (microCT) is through classic parametric (e.g., ANOVA, Student's T-test) and nonparametric (e.g., Mann-Whitney U test) statistical tests and the reporting of p-values to indicate significance. However, on their own, these univariate assessments of significance fall prey to a number of weaknesses, including an increased chance of Type 1 error from multiple comparisons. Machine learning classification methods (e.g., unsupervised, k-means cluster analysis and supervised Support Vector Machine classification, SVM) simultaneously utilize an entire dataset comprised of many cortical structure or trabecular microarchitecture measures, thus minimizing bias and Type 1 error that are generated through multiple testing. Through simultaneous evaluation of an entire dataset, k-means and SVM thus provide a complementary approach to classic statistical analysis and enable a more robust assessment of microCT measures.

Parliament2: Accurate structural variant calling at scale.

BackgroundStructural variants (SVs) are critical contributors to genetic diversity and genomic disease. To predict the phenotypic impact of SVs, there is a need for better estimates of both the occurrence and frequency of SVs, preferably from large, ethnically diverse cohorts. Thus, the current standard approach requires the use of short paired-end reads, which remain challenging to detect, especially at the scale of hundreds to thousands of samples.FindingsWe present Parliament2, a consensus SV framework that leverages multiple best-in-class methods to identify high-quality SVs from short-read DNA sequence data at scale. Parliament2 incorporates pre-installed SV callers that are optimized for efficient execution in parallel to reduce the overall runtime and costs. We demonstrate the accuracy of Parliament2 when applied to data from NovaSeq and HiSeq X platforms with the Genome in a Bottle (GIAB) SV call set across all size classes. The reported quality score per SV is calibrated across different SV types and size classes. Parliament2 has the highest F1 score (74.27%) measured across the independent gold standard from GIAB. We illustrate the compute performance by processing all 1000 Genomes samples (2,691 samples) in <1 day on GRCH38. Parliament2 improves the runtime performance of individual methods and is open source (https://github.com/slzarate/parliament2), and a Docker image, as well as a WDL implementation, is available.ConclusionParliament2 provides both a highly accurate single-sample SV call set from short-read DNA sequence data and enables cost-efficient application over cloud or cluster environments, processing thousands of samples.

Characterizing the transient electrocardiographic signature of ischemic stress using Laplacian Eigenmaps for dimensionality reduction

ObjectiveDespite a long history of ECG-based monitoring of acute ischemia quantified by several widely used clinical markers, the diagnostic performance of these metrics is not yet satisfactory, motivating a data-driven approach to leverage underutilized information in the electrograms. This study introduces a novel metric for acute ischemia, created using a machine learning technique known as Laplacian eigenmaps (LE), and compares the diagnostic and temporal performance of the LE metric against traditional metrics. MethodsThe LE technique uses dimensionality reduction of simultaneously recorded time signals to map them into an abstract space in a manner that highlights the underlying signal behavior. To evaluate the performance of an electrogram-based LE metric compared to current standard approaches, we induced episodes of transient, acute ischemia in large animals and captured the electrocardiographic response using up to 600 electrodes within the intramural and epicardial domains. ResultsThe LE metric generally detected ischemia earlier than all other approaches and with greater accuracy. Unlike other metrics derived from specific features of parts of the signals, the LE approach uses the entire signal and provides a data-driven strategy to identify features that reflect ischemia. ConclusionThe superior performance of the LE metric suggests there are underutilized features of electrograms that can be leveraged to detect the presence of acute myocardial ischemia earlier and more robustly than current methods. SignificanceThe earlier detection capabilities of the LE metric on the epicardial surface provide compelling motivation to apply the same approach to ECGs recorded from the body surface.

Potentials to breed for improved fibre digestibility in temperate Czech maize (Zea mays L.) germplasm

Cell wall digestibility is an important quality trait of modern silage maize cultivars. The symbiotic relationship between microbes and ruminant livestock enables the efficient upcycling of otherwise for human consumption unsuitable rumen digestible fibre or cell wall components into highly nutritious milk and meat. Before entering the Czech National List of Plant Varieties, new silage maize germplasm is extensively tested for different cell wall digestibility parameters. Recently published, the undigestible neutral detergent fibre (uNDF) cell wall digestibility approach promises even greater practical relevance. The aim of our study was, therefore, to assess the potential of the uNDF method, compared with current standard procedures, using a vast set of official Czech plant variety trial evaluations and Czech silage analyses from the 2018 cropping season. The uNDF method yielded a twice as high phenotypic standard deviation, compared with the current standard approaches. This is good news for plant breeders, official variety testing organisations, and farm professionals alike, enabeling faster variety improvement and simpler variety selection. On the other hand, due to the low differentiation potential, we discourage the use of the absolute lignin content when selecting for digestible silage maize varieties. Since between the digestibility traits enzymatic soluble organic substance (ELOS) and cellulase digestibility (DCS), a Pearson correlation close to one was observed, the substitution of one of these analytics by the uNDF method, may render valuable additional information in a highly economical manner.

Cancer cell detection device for the diagnosis of bladder cancer from urine

Bladder cancer is common and has one of the highest recurrence rates. Cystoscopy, the current gold standard diagnosis approach, has recently benefited from the introduction of blue light assisted photodynamic diagnostic (PDD). While blue light cystoscopy improves diagnostic sensitivity, it remains a costly and invasive approach. Here, we present a microfluidic-based platform for non-invasive diagnosis which combines the principle of PDD with whole cell immunocapture technology to detect bladder cancer cells shed in patient urine ex vivo. Initially, we demonstrate with model cell lines that our non-invasive approach achieves highly specific capture rates of bladder cancer cells based on their Epithelial Cell Adhesion Molecule expression (>90%) and detection by the intensity levels of Hexaminolevulinic Acid-induced Protoporphyrin IX fluorescence. Then, we show in a pilot study that the biosensor platform successfully discriminates histopathologically diagnosed cancer patients (n = 10) from non-cancer controls (n = 25). Our platform can support the development of a novel non-invasive diagnostic device for post treatment surveillance in patients with bladder cancer and cancer detection in patients with suspected bladder cancer.

A data-driven method to identify frequency boundaries in multichannel electrophysiology data

BackgroundElectrophysiological recordings of the brain often exhibit neural oscillations, defined as narrowband bumps that deviate from the background power spectrum. These narrowband dynamics are grouped into frequency ranges, and the study of how activities in these ranges are related to cognition and disease is a major part of the neuroscience corpus. Frequency ranges are nearly always defined according to integer boundaries, such as 4−8 Hz for the theta band and 8−12 Hz for the alpha band. New methodA data-driven multivariate method is presented to identify empirical frequency boundaries based on clustering of spatiotemporal similarities across a range of frequencies. The method, termed gedBounds, identifies patterns in covariance matrices that maximally separate narrowband from broadband activity, and then identifies clusters in the correlation matrix of those spatial patterns over all frequencies, using the dbscan clustering algorithm. Those clusters are empirically derived frequency bands, from which boundaries can be extracted. ResultsgedBounds recovers ground truth results in simulated data with high accuracy. The method was tested on EEG resting-state data from Parkinson’s patients and control, and several features of the frequency components differed between patients and controls. Comparison with existing methodsThe proposed method offers higher precision in defining subject-specific frequency boundaries compared to the current standard approach. ConclusionsgedBounds can increase the precision and feature extraction of spectral dynamics in electrophysiology data.