Abstract
Physicians taking care of patients with COVID-19 have described different changes in routine blood parameters. However, these changes hinder them from performing COVID-19 diagnoses. We ...constructed a machine learning model for COVID-19 diagnosis that was based and cross-validated on the routine blood tests of 5333 patients with various bacterial and viral infections, and 160 COVID-19-positive patients. We selected the operational ROC point at a sensitivity of 81.9% and a specificity of 97.9%. The cross-validated AUC was 0.97. The five most useful routine blood parameters for COVID-19 diagnosis according to the feature importance scoring of the XGBoost algorithm were: MCHC, eosinophil count, albumin, INR, and prothrombin activity percentage. t-SNE visualization showed that the blood parameters of the patients with a severe COVID-19 course are more like the parameters of a bacterial than a viral infection. The reported diagnostic accuracy is at least comparable and probably complementary to RT-PCR and chest CT studies. Patients with fever, cough, myalgia, and other symptoms can now have initial routine blood tests assessed by our diagnostic tool. All patients with a positive COVID-19 prediction would then undergo standard RT-PCR studies to confirm the diagnosis. We believe that our results represent a significant contribution to improvements in COVID-19 diagnosis.
A study of natively iodinated bovine thyroglobulin demonstrates that structural details of biologically important chemical reactions can now be visualized by electron cryo‐microscopy.
A study of ...natively iodinated bovine thyroglobulin demonstrates that structural details of biologically important chemical reactions can now be visualized by electron cryo‐microscopy.
The function of oligomeric proteins is inherently linked to their quaternary structure. In the absence of high-resolution data, low-resolution information in the form of spatial restraints can ...significantly contribute to the precision and accuracy of structural models obtained using computational approaches. To obtain such restraints, chemical cross-linking coupled with mass spectrometry (XL-MS) is commonly used. However, the use of XL-MS in the modeling of protein complexes comprised of identical subunits (homo-oligomers) is often hindered by the inherent ambiguity of intra- and inter-subunit connection assignment.
We present a comprehensive evaluation of (1) different methods for inter-residue distance calculations, and (2) different approaches for the scoring of spatial restraints. Our results show that using Solvent Accessible Surface distances (SASDs) instead of Euclidean distances (EUCs) greatly reduces the assignation ambiguity and delivers better modeling precision. Furthermore, ambiguous connections should be considered as inter-subunit only when the intra-subunit alternative exceeds the distance threshold. Modeling performance can also be improved if symmetry, characteristic for most homo-oligomers, is explicitly defined in the scoring function.
Our findings provide guidelines for proper evaluation of chemical cross-linking-based spatial restraints in modeling homo-oligomeric protein complexes, which could facilitate structural characterization of this important group of proteins.
Routine blood test results are assumed to contain much more information than is usually recognised even by the most experienced clinicians. Using routine blood tests from 15,176 neurological patients ...we built a machine learning predictive model for the diagnosis of brain tumours. We validated the model by retrospective analysis of 68 consecutive brain tumour and 215 control patients presenting to the neurological emergency service. Only patients with head imaging and routine blood test data were included in the validation sample. The sensitivity and specificity of the adapted tumour model in the validation group were 96% and 74%, respectively. Our data demonstrate the feasibility of brain tumour diagnosis from routine blood tests using machine learning. The reported diagnostic accuracy is comparable and possibly complementary to that of imaging studies. The presented machine learning approach opens a completely new avenue in the diagnosis of these grave neurological diseases and demonstrates the utility of valuable information obtained from routine blood tests.
In-process monitoring of glycosylated protein concentration becomes very important with the introduction of perfusion bioprocesses. Affinity chromatography based on lectins allows selective ...monitoring when carbohydrates are accessible on the protein surface. In this work, we immobilized lectin on polyHIPE type of monoliths and implemented it for bioprocess monitoring. A spacer was introduced to lectin, which increased binding kinetics toward Fc-fusion protein, demonstrated by bio-layer interferometry. Furthermore, complete desorption using 0.25 M galactose was shown. Affinity column exhibited linearity in the range between 0.5 and 8 mg/ml and flow-unaffected binding for the flow-rates between 0.5 and 8 ml/min. Long-term stability over at least four months period was demonstrated. No unspecific binding of culture media components, including host cell proteins and DNA, was detected. Results obtained by affinity column matched concentration values obtained by a reference method.
The gene-for-gene mechanism of plant disease resistance involves direct or indirect recognition of pathogen avirulence (Avr) proteins by plant resistance (R) proteins. Flax rust (Melampsora lini) ...AvrL567 avirulence proteins and the corresponding flax (Linum usitatissimum) L5, L6, and L7 resistance proteins interact directly. We determined the three-dimensional structures of two members of the AvrL567 family, AvrL567-A and AvrL567-D, at 1.4- and 2.3-Å resolution, respectively. The structures of both proteins are very similar and reveal a β-sandwich fold with no close known structural homologs. The polymorphic residues in the AvrL567 family map to the surface of the protein, and polymorphisms in residues associated with recognition differences for the R proteins lead to significant changes in surface chemical properties. Analysis of single amino acid substitutions in AvrL567 proteins confirm the role of individual residues in conferring differences in recognition and suggest that the specificity results from the cumulative effects of multiple amino acid contacts. The structures also provide insights into possible pathogen-associated functions of AvrL567 proteins, with nucleic acid binding activity demonstrated in vitro. Our studies provide some of the first structural information on avirulence proteins that bind directly to the corresponding resistance proteins, allowing an examination of the molecular basis of the interaction with the resistance proteins as a step toward designing new resistance specificities.
Quick and accurate medical diagnoses are crucial for the successful treatment of diseases. Using machine learning algorithms and based on laboratory blood test results, we have built two models to ...predict a haematologic disease. One predictive model used all the available blood test parameters and the other used only a reduced set that is usually measured upon patient admittance. Both models produced good results, obtaining prediction accuracies of 0.88 and 0.86 when considering the list of five most likely diseases and 0.59 and 0.57 when considering only the most likely disease. The models did not differ significantly, which indicates that a reduced set of parameters can represent a relevant "fingerprint" of a disease. This knowledge expands the model's utility for use by general practitioners and indicates that blood test results contain more information than physicians generally recognize. A clinical test showed that the accuracy of our predictive models was on par with that of haematology specialists. Our study is the first to show that a machine learning predictive model based on blood tests alone can be successfully applied to predict haematologic diseases. This result and could open up unprecedented possibilities for medical diagnosis.
The growing threat of antibiotic resistance necessitates accurate differentiation between bacterial and viral infections for proper antibiotic administration. In this study, a Virus vs. Bacteria ...machine learning model was developed to distinguish between these infection types using 16 routine blood test results, C-reactive protein concentration (CRP), biological sex, and age. With a dataset of 44,120 cases from a single medical center, the model achieved an accuracy of 82.2 %, a sensitivity of 79.7 %, a specificity of 84.5 %, a Brier score of 0.129, and an area under the ROC curve (AUC) of 0.905, outperforming a CRP-based decision rule. Notably, the machine learning model enhanced accuracy within the CRP range of 10–40 mg/L, a range where CRP alone is less informative. These results highlight the advantage of integrating multiple blood parameters in diagnostics. The "Virus vs. Bacteria" model paves the way for advanced diagnostic tools, leveraging machine learning to optimize infection management.
Here we present the tetrameric structure of stefin B, which is the result of a process by which two domain-swapped dimers of stefin B are transformed into tetramers. The transformation involves a ...previously unidentified process of extensive intermolecular contacts, termed hand shaking, which occurs concurrently with
trans to
cis isomerization of proline 74. This proline residue is widely conserved throughout the cystatin superfamily, a member of which, human cystatin C, is the key protein in cerebral amyloid angiopathy. These results are consistent with the hypothesis that isomerization of proline residues can play a decisive role in amyloidogenesis.
EpCAM (epithelial cell adhesion molecule), a stem and carcinoma cell marker, is a cell surface protein involved in homotypic cell-cell adhesion via intercellular oligomerization and proliferative ...signalling via proteolytic cleavage. Despite its use as a diagnostic marker and being a drug target, structural details of this conserved vertebrate-exclusive protein remain unknown. Here we present the crystal structure of a heart-shaped dimer of the extracellular part of human EpCAM. The structure represents a cis-dimer that would form at cell surfaces and may provide the necessary structural foundation for the proposed EpCAM intercellular trans-tetramerization mediated by a membrane-distal region. By combining biochemical, biological and structural data on EpCAM, we show how proteolytic processing at various sites could influence structural integrity, oligomeric state and associated functionality of the molecule. We also describe the epitopes of this therapeutically important protein and explain the antigenicity of its regions.
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