Repeated Lung Ultrasound versus Chest X-ray-Which One Predicts Better Clinical Outcome in COVID-19?

The purpose of this study was to evaluate whether changes in repeated lung ultrasound (LUS) or chest X-ray (CXR) of coronavirus disease 2019 (COVID-19) patients can predict the development of severe disease and the need for treatment in the intensive care unit (ICU). In this prospective monocentric study, COVID-19 patients received standardized LUS and CXR at day 1, 3 and 5. Scores for changes in LUS (LUS score) and CXR (RALE and M-RALE) were calculated and compared. Intra-class correlation was calculated for two readers of CXR and ROC analysis to evaluate the best discriminator for the need for ICU treatment. A total of 30 patients were analyzed, 26 patients with follow-up LUS and CXR. Increase in M-RALE between baseline and follow-up 1 was significantly higher in patients with need for ICU treatment in the further hospital stay (p = 0.008). Both RALE and M-RALE significantly correlated with LUS score (r = 0.5, p < 0.0001). ROC curves with need for ICU treatment as separator were not significantly different for changes in M-RALE (AUC: 0.87) and LUS score (AUC: 0.79), both being good discriminators. ICC was moderate for RALE (0.56) and substantial for M-RALE (0.74). The present study demonstrates that both follow-up LUS and CXR are powerful tools to track the evolution of COVID-19, and can be used equally as predictors for the need for ICU treatment.

Conformational multiplicity of bacterial ferric binding protein revealed by small angle x-ray scattering and molecular dynamics calculations.

This study combines molecular dynamics (MD) simulations with small angle x-ray scattering (SAXS) measurements to investigate the range of conformations that can be adopted by a pH/ionic strength (IS) sensitive protein and to quantify its distinct populations in solution. To explore how the conformational distribution of proteins may be modified in the environmental niches of biological media, we focus on the periplasmic ferric binding protein A (FbpA) from Haemophilus influenzae involved in the mechanism by which bacteria capture iron from higher organisms. We examine iron-binding/release mechanisms of FbpA in varying conditions simulating its biological environment. While we show that these changes fall within the detectable range for SAXS as evidenced by differences observed in the theoretical scattering patterns calculated from the crystal structure models of apo and holo forms, detection of conformational changes due to the point mutation D52A and changes in ionic strength (IS) from SAXS scattering profiles have been challenging. Here, to reach conclusions, statistical analyses with SAXS profiles and results from different techniques were combined in a complementary fashion. The SAXS data complemented by size exclusion chromatography point to multiple and/or alternative conformations at physiological IS, whereas they are well-explained by single crystallographic structures in low IS buffers. By fitting the SAXS data with unique conformations sampled by a series of MD simulations under conditions mimicking the buffers, we quantify the populations of the occupied substates. We also find that the D52A mutant that we predicted by coarse-grained computational modeling to allosterically control the iron binding site in FbpA, responds to the environmental changes in our experiments with conformational selection scenarios that differ from those of the wild type.

Biases associated with database structure for COVID-19 detection in X-ray images.

Several artificial intelligence algorithms have been developed for COVID-19-related topics. One that has been common is the COVID-19 diagnosis using chest X-rays, where the eagerness to obtain early results has triggered the construction of a series of datasets where bias management has not been thorough from the point of view of patient information, capture conditions, class imbalance, and careless mixtures of multiple datasets. This paper analyses 19 datasets of COVID-19 chest X-ray images, identifying potential biases. Moreover, computational experiments were conducted using one of the most popular datasets in this domain, which obtains a 96.19% of classification accuracy on the complete dataset. Nevertheless, when evaluated with the ethical tool Aequitas, it fails on all the metrics. Ethical tools enhanced with some distribution and image quality considerations are the keys to developing or choosing a dataset with fewer bias issues. We aim to provide broad research on dataset problems, tools, and suggestions for future dataset developments and COVID-19 applications using chest X-ray images.

Comparison of bioimpedance equations and dual-energy X-ray for assessment of fat free mass in a Chinese dialysis population.

PURPOSE: Bioelectrical impedance analysis (BIA) is simple, noninvasive, inexpensive and frequently used for estimating fat free mass (FFM). The aims of this study were to evaluate the applicability of different BIA equations on FFM in Chinese subjects, and to compare the difference in hemodialysis and peritoneal dialysis patients with healthy controls respectively. METHODS: Dialysis patients and healthy adults were enrolled in this study, and the subjects were matched by age, gender, and the minimum sample size in each group was calculated using PASS. FFM estimated by BIA was calculated using equations of Kyle, Sun SS and Segal, and TBW/0.73. Dual-energy X-ray absorptiometry (DXA) method was set as reference method. Pearson's correlation and Bland-Altman analysis were used to test the validity of the BIA equations. RESULTS: 50 hemodialysis (HD) patients, 52 peritoneal dialysis (PD) patients and 30 healthy adults aged 22-67 y were included in this study. Age, height, weight, BMI and gender did not differ significantly among HD, PD patients, and healthy controls (p > 0.05), but BIA parameters were quite different (p＜0.01). Bland-Altman analysis showed that in healthy volunteers, all equations showed good agreement with DXA measured. For dialysis patients, the FFM predictions of different equations showed differences between HD and PD patients, and the equations seemed more applicable for HD patients. CONCLUSION: The equations developed by healthy subjects might be not appropriate for dialysis patients, especially peritoneal dialysis patients. It is recommended to develop a specific BIA equation from dialysis population.

Unsupervised anomaly detection for posteroanterior chest X-rays using multiresolution patch-based self-supervised learning.

The demand for anomaly detection, which involves the identification of abnormal samples, has continued to increase in various domains. In particular, with increases in the data volume of medical imaging, the demand for automated screening systems has also risen. Consequently, in actual clinical practice, radiologists can focus only on diagnosing patients with abnormal findings. In this study, we propose an unsupervised anomaly detection method for posteroanterior chest X-rays (CXR) using multiresolution patch-based self-supervised learning. The core aspect of our approach is to leverage patch images of different sizes for training and testing to recognize diverse anomalies characterized by unknown shapes and scales. In addition, self-supervised contrastive learning is applied to learn the generalized and robust features of the patches. The performance of the proposed method is evaluated using posteroanterior CXR images from a public dataset for training and testing. The results show that the proposed method is superior to state-of-the-art anomaly detection methods. In addition, unlike single-resolution patch-based methods, the proposed method consistently exhibits a good overall performance regardless of the evaluation criteria used for comparison, thus demonstrating the effectiveness of using multiresolution patch-based features. Overall, the results of this study validate the effectiveness of multiresolution patch-based self-supervised learning for detecting anomalies in CXR images.

Calculated and measured radiation dose for the low energy xoft axxent eBT X-ray source.

PURPOSE: In this study, it was aimed to evaluate the functionality to deliver different prescription dose except 20 Gy for the Xoft Axxent Ebt (electronic Brachytherapy) system and analyzing the system in terms of radiation dosimetry in water and 0.9% isotonic Sodium Chloride (NaCl) solution. MATERIALS AND METHODS: In the Xoft Axxent eBT, different prescription dose in single fraction were calculated for different balloon applicator volumes based on source position and irradiation times. EBT-XD Gafchromic film was calibrated at 6MV photon energy. A balloon applicator filled with 0.9% isotonic NaCl solution was used to deliver a radiation dose of 20 Gy, 16 Gy, 10 Gy on the applicator surface. Then the balloon applicator was filled with water and the same measurements were repeated. Finally, the balloon applicator was irradiated by positioning it at different distances in the water phantom to simulate the isodose contour. RESULTS: At the time the balloon applicator was filled with water and 0,9% NaCl solution, the difference between the planned dose and the absorbed dose was ~ 2% vs. 15% for 30 cc, ~ 5% vs. 14% for 35 cc and ~ 3,5% vs. 10% for 40 cc respectively. Finally, the absorbed dose at a distance of 1 cm from the applicator surface was measured as 9.63 Gy. CONCLUSION: In this study, it was showed that different prescription dose could be possible to deliver in the Xoft Axxent eBT system based on the standard plan. In addition, the absorbed dose was higher than the planned dose depending on the effective atomic number of NaCl solution comparing to water due to photoelectric effect in low energy photons. By measuring the dose distributions at different distances from the balloon applicator surface, the absorbed dose in tissue equivalent medium was determined and the isodose contours characteristics was simulated.

Quantitative analysis of calcium oxalate monohydrate and dihydrate for elucidating the formation mechanism of calcium oxalate kidney stones.

We sought to identify and quantitatively analyze calcium oxalate (CaOx) kidney stones on the order of micrometers, with a focus on the quantitative identification of calcium oxalate monohydrate (COM) and dihydrate (COD). We performed Fourier transform infrared (FTIR) spectroscopy, powder X-ray diffraction (PXRD), and microfocus X-ray computed tomography measurements (microfocus X-ray CT) and compared their results. An extended analysis of the FTIR spectrum focusing on the 780 cm-1 peak made it possible to achieve a reliable analysis of the COM/COD ratio. We succeeded in the quantitative analysis of COM/COD in 50-µm2 areas by applying microscopic FTIR for thin sections of kidney stones, and by applying microfocus X-ray CT system for bulk samples. The analysis results based on the PXRD measurements with micro-sampling, the microscopic FTIR analysis of thin sections, and the microfocus X-ray CT system observation of a bulk kidney stone sample showed roughly consistent results, indicating that all three methods can be used complementarily. This quantitative analysis method evaluates the detailed CaOx composition on the preserved stone surface and provides information on the stone formation processes. This information clarifies where and which crystal phase nucleates, how the crystals grow, and how the transition from the metastable phase to the stable phase proceeds. The phase transition affects the growth rate and hardness of kidney stones and thus provides crucial clues to the kidney stone formation process.

Robust total X-ray scattering workflow to study correlated motion of proteins in crystals.

The breathing motions of proteins are thought to play a critical role in function. However, current techniques to study key collective motions are limited to spectroscopy and computation. We present a high-resolution experimental approach based on the total scattering from protein crystals at room temperature (TS/RT-MX) that captures both structure and collective motions. To reveal the scattering signal from protein motions, we present a general workflow that enables robust subtraction of lattice disorder. The workflow introduces two methods: GOODVIBES, a detailed and refinable lattice disorder model based on the rigid-body vibrations of a crystalline elastic network; and DISCOBALL, an independent method of validation that estimates the displacement covariance between proteins in the lattice in real space. Here, we demonstrate the robustness of this workflow and further demonstrate how it can be interfaced with MD simulations towards obtaining high-resolution insight into functionally important protein motions.

Evaluation of the clinical performance of an AI-based application for the automated analysis of chest X-rays.

The AI-Rad Companion Chest X-ray (AI-Rad, Siemens Healthineers) is an artificial-intelligence based application for the analysis of chest X-rays. The purpose of the present study is to evaluate the performance of the AI-Rad. In total, 499 radiographs were retrospectively included. Radiographs were independently evaluated by radiologists and the AI-Rad. Findings indicated by the AI-Rad and findings described in the written report (WR) were compared to the findings of a ground truth reading (consensus decision of two radiologists after assessing additional radiographs and CT scans). The AI-Rad can offer superior sensitivity for the detection of lung lesions (0.83 versus 0.52), consolidations (0.88 versus 0.78) and atelectasis (0.54 versus 0.43) compared to the WR. However, the superior sensitivity is accompanied by higher false-detection-rates. The sensitivity of the AI-Rad for the detection of pleural effusions is lower compared to the WR (0.74 versus 0.88). The negative-predictive-values (NPV) of the AI-Rad for the detection of all pre-defined findings are on a high level and comparable to the WR. The seemingly advantageous high sensitivity of the AI-Rad is partially offset by the disadvantage of a high false-detection-rate. At the current stage of development, therefore, the high NPVs may be the greatest benefit of the AI-Rad giving radiologists the possibility to re-insure their own negative search for pathologies and thus boosting their confidence in their reports.

Deep Learning Algorithms with LIME and Similarity Distance Analysis on COVID-19 Chest X-ray Dataset.

In the last few years, many types of research have been conducted on the most harmful pandemic, COVID-19. Machine learning approaches have been applied to investigate chest X-rays of COVID-19 patients in many respects. This study focuses on the deep learning algorithm from the standpoint of feature space and similarity analysis. Firstly, we utilized Local Interpretable Model-agnostic Explanations (LIME) to justify the necessity of the region of interest (ROI) process and further prepared ROI via U-Net segmentation that masked out non-lung areas of images to prevent the classifier from being distracted by irrelevant features. The experimental results were promising, with detection performance reaching an overall accuracy of 95.5%, a sensitivity of 98.4%, a precision of 94.7%, and an F1 score of 96.5% on the COVID-19 category. Secondly, we applied similarity analysis to identify outliers and further provided an objective confidence reference specific to the similarity distance to centers or boundaries of clusters while inferring. Finally, the experimental results suggested putting more effort into enhancing the low-accuracy subspace locally, which is identified by the similarity distance to the centers. The experimental results were promising, and based on those perspectives, our approach could be more flexible to deploy dedicated classifiers specific to different subspaces instead of one rigid end-to-end black box model for all feature space.

Characterization of Systemic Disease Development and Paw Inflammation in a Susceptible Mouse Model of Mayaro Virus Infection and Validation Using X-ray Synchrotron Microtomography.

Mayaro virus (MAYV) is an emerging arthropod-borne virus endemic in Latin America and the causative agent of arthritogenic febrile disease. Mayaro fever is poorly understood; thus, we established an in vivo model of infection in susceptible type-I interferon receptor-deficient mice (IFNAR-/-) to characterize the disease. MAYV inoculations in the hind paws of IFNAR-/- mice result in visible paw inflammation, evolve into a disseminated infection and involve the activation of immune responses and inflammation. The histological analysis of inflamed paws indicated edema at the dermis and between muscle fibers and ligaments. Paw edema affected multiple tissues and was associated with MAYV replication, the local production of CXCL1 and the recruitment of granulocytes and mononuclear leukocytes to muscle. We developed a semi-automated X-ray microtomography method to visualize both soft tissue and bone, allowing for the quantification of MAYV-induced paw edema in 3D with a voxel size of 69 µm3. The results confirmed early edema onset and spreading through multiple tissues in inoculated paws. In conclusion, we detailed features of MAYV-induced systemic disease and the manifestation of paw edema in a mouse model extensively used to study infection with alphaviruses. The participation of lymphocytes and neutrophils and expression of CXCL1 are key features in both systemic and local manifestations of MAYV disease.

Effects of synchrotron-based X-rays and gold nanoparticles on normal and cancer cell morphology and migration.

It has been shown lately that gold nanoparticles (AuNPs) and ionizing radiation (IR) have inhibitory effects on cancer cell migration while having promoting effects on normal cells' motility. Also, IR increases cancer cell adhesion with no significant effects on normal cells. In this study, synchrotron-based microbeam radiation therapy, as a novel pre-clinical radiotherapy protocol, is employed to investigate the effects of AuNPs on cell migration. Experiments were conducted utilizing synchrotron X-rays to investigate cancer and normal cell morphology and migration behaviour when they are exposed to synchrotron broad beams (SBB) and synchrotron microbeams (SMB). This in vitro study was conducted in two phases. In phase I two cancer cell lines - human prostate (DU145) and human lung (A549) - were exposed to various doses of SBB and SMB. Based on the phase I results, in phase II two normal cell lines were studied: human epidermal melanocytes (HEM) and human primary colon epithelial (CCD841), along with their respective cancerous counterparts, human primary melanoma (MM418-C1) and human colorectal adenocarcinoma (SW48). The results show that radiation-induced damage in cells' morphology becomes visible with SBB at doses greater than 50 Gy, and incorporating AuNPs increases this effect. Interestly, under the same conditions, no visible morphological changes were observed in the normal cell lines post-irradiation (HEM and CCD841). This can be attributed to the differences in cell metabolic and reactive oxygen species levels between normal and cancer cells. The outcome of this study highlights future applications of synchrotron-based radiotherapy, where it is possible to deliver extremely high doses to cancer tissues whilst preserving surrounding normal tissues from radiation-induced damage.

Development of serial X-ray fluorescence holography for radiation-sensitive protein crystals.

X-ray fluorescence holography (XFH) is a powerful atomic resolution technique capable of directly imaging the local atomic structure around atoms of a target element within a material. Although it is theoretically possible to use XFH to study the local structures of metal clusters in large protein crystals, the experiment has proven difficult to perform, especially on radiation-sensitive proteins. Here, the development of serial X-ray fluorescence holography to allow the direct recording of hologram patterns before the onset of radiation damage is reported. By combining a 2D hybrid detector and the serial data collection used in serial protein crystallography, the X-ray fluorescence hologram can be directly recorded in a fraction of the measurement time needed for conventional XFH measurements. This approach was demonstrated by obtaining the Mn Kα hologram pattern from the protein crystal Photosystem II without any X-ray-induced reduction of the Mn clusters. Furthermore, a method to interpret the fluorescence patterns as real-space projections of the atoms surrounding the Mn emitters has been developed, where the surrounding atoms produce large dark dips along the emitter-scatterer bond directions. This new technique paves the way for future experiments on protein crystals that aim to clarify the local atomic structures of their functional metal clusters, and for other related XFH experiments such as valence-selective XFH or time-resolved XFH.

Evaluation of quantitative synchrotron radiation micro-X-ray fluorescence in rice grain.

Concentrations of nutrients and contaminants in rice grain affect human health, specifically through the localization and chemical form of elements. Methods to spatially quantify the concentration and speciation of elements are needed to protect human health and characterize elemental homeostasis in plants. Here, an evaluation was carried out using quantitative synchrotron radiation microprobe X-ray fluorescence (SR-µXRF) imaging by comparing average rice grain concentrations of As, Cu, K, Mn, P, S and Zn measured with rice grain concentrations from acid digestion and ICP-MS analysis for 50 grain samples. Better agreement was found between the two methods for high-Z elements. Regression fits between the two methods allowed quantitative concentration maps of the measured elements. These maps revealed that most elements were concentrated in the bran, although S and Zn permeated into the endosperm. Arsenic was highest in the ovular vascular trace (OVT), with concentrations approaching 100 mg kg-1 in the OVT of a grain from a rice plant grown in As-contaminated soil. Quantitative SR-µXRF is a useful approach for comparison across multiple studies but requires careful consideration of sample preparation and beamline characteristics.

Assessing the prospect of XAFS experiments of metalloproteins under in vivo conditions at Indus-2 synchrotron facility, India.

The feasibility of X-ray absorption fine-structure (XAFS) experiments of ultra-dilute metalloproteins under in vivo conditions (T = 300 K, pH = 7) at the BL-9 bending-magnet beamline (Indus-2) is reported, using as an example analogous synthetic Zn (0.1 mM) M1dr solution. The (Zn K-edge) XAFS of M1dr solution was measured with a four-element silicon drift detector. The first-shell fit was tested and found to be robust against statistical noise, generating reliable nearest-neighbor bond results. The results are found to be invariant between physiological and non-physiological conditions, which confirms the robust coordination chemistry of Zn with important biological implications. The scope of improving spectral quality for accommodation of higher-shell analysis is addressed.

Sample-minimizing co-flow cell for time-resolved pump-probe X-ray solution scattering.

A fundamental problem in biological sciences is understanding how macromolecular machines work and how the structural changes of a molecule are connected to its function. Time-resolved techniques are vital in this regard and essential for understanding the structural dynamics of biomolecules. Time-resolved small- and wide-angle X-ray solution scattering has the capability to provide a multitude of information about the kinetics and global structural changes of molecules under their physiological conditions. However, standard protocols for such time-resolved measurements often require significant amounts of sample, which frequently render time-resolved measurements impossible. A cytometry-type sheath co-flow cell, developed at the BioCARS 14-ID beamline at the Advanced Photon Source, USA, allows time-resolved pump-probe X-ray solution scattering measurements to be conducted with sample consumption reduced by more than ten times compared with standard sample cells and protocols. The comparative capabilities of the standard and co-flow experimental setups were demonstrated by studying time-resolved signals in photoactive yellow protein.

Lesion detection of chest X-Ray based on scalable attention residual CNN.

Most of the research on disease recognition in chest X-rays is limited to segmentation and classification, but the problem of inaccurate recognition in edges and small parts makes doctors spend more time making judgments. In this paper, we propose a lesion detection method based on a scalable attention residual CNN (SAR-CNN), which uses target detection to identify and locate diseases in chest X-rays and greatly improves work efficiency. We designed a multi-convolution feature fusion block (MFFB), tree-structured aggregation module (TSAM), and scalable channel and spatial attention (SCSA), which can effectively alleviate the difficulties in chest X-ray recognition caused by single resolution, weak communication of features of different layers, and lack of attention fusion, respectively. These three modules are embeddable and can be easily combined with other networks. Through a large number of experiments on the largest public lung chest radiograph detection dataset, VinDr-CXR, the mean average precision (mAP) of the proposed method was improved from 12.83% to 15.75% in the case of the PASCAL VOC 2010 standard, with IoU > 0.4, which exceeds the existing mainstream deep learning model. In addition, the proposed model has a lower complexity and faster reasoning speed, which is conducive to the implementation of computer-aided systems and provides referential solutions for relevant communities.

Automated prediction of COVID-19 severity upon admission by chest X-ray images and clinical metadata aiming at accuracy and explainability.

In the past few years COVID-19 posed a huge threat to healthcare systems around the world. One of the first waves of the pandemic hit Northern Italy severely resulting in high casualties and in the near breakdown of primary care. Due to these facts, the Covid CXR Hackathon-Artificial Intelligence for Covid-19 prognosis: aiming at accuracy and explainability challenge had been launched at the beginning of February 2022, releasing a new imaging dataset with additional clinical metadata for each accompanying chest X-ray (CXR). In this article we summarize our techniques at correctly diagnosing chest X-ray images collected upon admission for severity of COVID-19 outcome. In addition to X-ray imagery, clinical metadata was provided and the challenge also aimed at creating an explainable model. We created a best-performing, as well as, an explainable model that makes an effort to map clinical metadata to image features whilst predicting the prognosis. We also did many ablation studies in order to identify crucial parts of the models and the predictive power of each feature in the datasets. We conclude that CXRs at admission do not help the predicting power of the metadata significantly by itself and contain mostly information that is also mutually present in the blood samples and other clinical factors collected at admission.