dataesr/scientific-paragraphs-categorization huggingface dataset:
Metformin and Paclitaxel Bind to Related Protein Signaling Pathway AMPK modulation (downstream and/or upstream regulatory proteins of cell signaling pathways involved in cell survival and proliferation) is the biological mechanism by which PXT and MET modulate the activity of apoptosis and autophagy. The complexes were docked, and the PLIP web server was used to evaluate them. The possible docking modes of protein-ligand interaction were calculated and displayed by the molecular docking server based on AutoDock 4. This enables a molecular understanding of the interaction between PXT and MET 3D structures imported from the PubChem database and the crystal structures of protein kinases (AMPK, EGFRK (PDB ID: 4EAG), 1M17); autophagy-related proteins such as FKBP12-rapamycin-mTOR-FRB, the PPIase domain of FKBP51-mTOR-FRB, Beclin 1, and ATG16L1 ATG4A (PDB ID: 1FAP, 4DRH, 3Q8T, 5NUV, 2P82); and apoptosis-related proteins (TP53, Caspase3, (PDB ID: 3DCY, 3GJQ)), which were imported from the RCSB protein data bank.
Ligands docked with the target protein generated using PLIP. The ligands used were b MET and f PXT. The 2D plot of the interaction of MET and PXT with active residues at EGFRK's active site (e. g. , CYS-773(2), PHE-771, TYR-777) and (e. g. , ASP-776), respectively, through hydrogen bonding (HB) and with active residues at EGFRK's active site (e. g. , LEU-694, PHE-699(2), VAL-702(3), ALA-719, ARG-817, LEU820) and (e. g. , THR-88, TYR-120, GLN-122, LYS-126, VAL-129(2), ILE-149(2), HIS-150, ARG-223), respectively, through hydrophobic bonding. HB plot with active residues involved in EGFRK-MET and PXT interactions. (C). Molecular docking analysis of the TP53-induced glycolysis and apoptosis regulator protein with MET and PXT. The 3D modeling of the crystal structure of apoptosis regulator protein (PDB ID: 3DCY) with MET and PXT interacting active residues of apoptosis regulator protein.
Ligands docked with the target protein generated using PLIP. The ligands used were b MET and f PXT. The 2D plot of the interaction of MET with active residues at the apoptosis regulator protein's active site (e. g. , PRO-30, LEU-31(2), GLU-29) and (e. g. , ARG-15, PHE-36, SER-32, GLU-33) through hydrogen bonding (HB) and hydrophobic bonding, respectively. The 2D plot of the interaction of PXT with active residues at the protein's active site (e. g. , ASP28,85, LEU59, LYS63, GLN64, HIS67, TYR83), respectively, through hydrophobic bonding. HB plot with active residues involved in TP53-induced apoptosis regulator protein-MET and PXT interactions. (D) Molecular docking analysis of caspase3 apoptosis protein with MET and PXT. The 3D modeling of the crystal structure of caspase3 protein (PDB ID: 3GJQ) with MET and PXT interacting with active residues of the caspase3 protein.
Ligands docked with the target protein, generated using PLIP. The ligands used were MET and PXT. The 2D plot of the interaction of MET and PXT with active residues at the caspase3 apoptosis protein's active site (e. g. , SER-63, SER-65(3), THR-62, HIS-121) and (e. g. , THR-62(2), SER-65), respectively, through hydrogen bonding (HB) and with active residues at caspase3 apoptosis protein's active site (e. g. , ARG-64) and (e. g. , ARG-64, CYS-163, HIS-121, SER-65, ASP-70, GLN-161), respectively, through hydrophobic bonding. HB plot with active residues involved in caspase3 apoptosis protein-MET and PXT interactions. (E). Molecular docking analysis of Beclin 1, an essential autophagy protein, with MET and PXT. The 3D modeling of the crystal structure of Beclin 1 (PDB ID: 3Q8T) with MET and PXT interacting with active residues of Beclin 1 protein.
This research was funded by the  Deanship of Scientific Research (DSR)  at  King Abdulaziz University, Jeddah, Saudi Arabia  under-grant  ID J-18-130-1434. Institutional Review Board Statement: Not applicable. : Not applicable. This project was funded by the  Deanship of Scientific Research (DSR)  at  King Abdulaziz University, Jeddah, Saudi Arabia  under-grant  ID J-18-130-1434. The authors, therefore, acknowledge with thanks  DSR  for technical and financial support. The authors would like to express their gratitude to the  Biochemistry Department, Faculty of Science, King Abdulaziz University  as well as the tissue culture and molecular biology labs of the  King Fahd Medical Research Centre.
ACKNOWLEDGEMENTS This work was supported by the  European Space Agency (ESA). This perspective paper is based upon work from the Contributors to the  Topic C "Plant Biology " of the  ESA SciSpacE  white paper " Biology in Space and Analogue Environments ", listed in alphabetic order:  Giovanna Aronne ,  Nicol Caplin ,  Eugénie Carnero-Diaz ,  Raúl Herranz ,  Nele Horemans ,  Ann-Iren Kittang Jost  (Coordinator),  Valerie Legue ,  F. Javier Medina ,  Veronica De Micco , (Coordinator),  Veronica Pereda-Campos ,  Mona Schiefloe.
IMP, investigational medicinal products; NAFLD, non-alcoholic fatty liver disease; NASH, non-alcoholic steatohepatitis. support  EU-PEARL  has received funding from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement No  853966-2. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme and EFPIA  and  Children's Tumor Foundation ,  Global Alliance for TB Drug Development Non-profit Organisation, Springworks Therapeutics Inc.
Acknowledgements NASH EU-PEARL investigators:  Nicholas A. Di Prospero ,  Vlad Ratziu ,  Juan M. Pericàs ,  Mette Skalshøj Kjaer ,  Quentin M. Anstee ,  Frank Tacke ,  Peter Mesenbrinck ,  Jesús Rivera-Esteban ,  Franz Koenig ,  Elena Sena ,  Ramiro Manzano ,  Joan Genescà ,  Raluca Pais ,  Leila Kara ,  Elias Meyer ,  Anna Duca ,  Timothy Kline ,  Anders Aaes-Jørgensen ,  Tania Balthaus ,  Natalie de Preville ,  Lingjiao Zhang ,  George Capuano ,  Salvatore Morello ,  Tobias Mielke ,  Sabina Hernandez Penna ,  Martin Posch. This publication reflects only the author's views. The JU is not responsible for any use that may be made of the information it contains.
Safety and Tolerance No serious adverse events have been reported during the study. Sixty-seven adverse events were reported and among them, 57 AEs were relating to expected adverse events that occurred frequently after dermal injections: the skin reactions were mostly  1102 concerned as ecchymosis (n=27, 40%), swelling (n=11, 16%), erythema (n=5, 7%), pain (n=4, 6%), irregularities on palpation (n=4, 6%), pain at palpation (n=2, 3%), pruritus (n=2, 3%), stinging (n=2, 3%), which disappeared later (none of the reported local reactions were still present at D60 n=69 nor D120 n=93 among the data collected). There was only one subject with palpable nodule and one subject with moderate pigmentation which disappeared before D30. The nonrelated adverse events were limited to one subject suffered from zona on one arm approximately two months after injections and one case had eyes dryness and stinging.
Most DMS modules are proprietary and connected to the dose index tracker without being independent of the vendor-specific dose database. Business analytics components in dose management systems have the same interoperability issue. Most of the radiation analytics tools and components have proprietary connectivity with a specific dose management system provider and cannot connect agnostically to different DMS providers. We design, test, and document multiple methods for radiation analytics to be agnostic and independent from the radiation dose index monitoring providers based on standardized Fast Healthcare Interoperability Resources (FHIR®) Application Programming Interfaces (API) connectivity.
It can be used to compare between radiation characteristics of population cohorts, or between cohorts of exam procedures, between facilities. Another application aspect of analytics is detection of anomalies related to over irradiation, which can be helpful to quickly take actions to avoid deterministic effects of irradiations and enable alerts. Many dose management systems have these functionalities on radiation data: data visualization, qualities measurement, analytics, alerting and reporting. However, accessing the data is a challenge. Most of the analytics tools used and described in the literature have direct and proprietary access to indexed data through the dose management system. Such proprietary access can be for example through directly accessing the database of the indexed radiation data, or through proprietary APIs to share radiation data.
For the scope of the implementation guide, only CT irradiation event was defined, but the Implementation Guide (IG) does not restrict the implementers from extending the resources to X-Ray irradiation events. The main targeted use case was the sharing of the radiation summary between the dose management system and the RIS, in order to respond to legal requirements and regulations around the world  [16, 17]. However, other use cases can be explored, like analytics and quality measurements. In fact, expressing radiation data through standardized FHIR® resources is very helpful to define agnostic methodologies for analytics and quality measurements. For instance, a possible exploitation would be centralizing the radiation data from multiple dose management systems in multiple facilities, in the same FHIR® server, for analytics purpose, as described in Fig.
Analytics on FHIR® Some open-source tools enable analytics on FHIR® resources  [25] [26] [27] , which can be exploited for radiation data statistics. For instance, Pathling  [25]  is a collection of libraries simplifying query, transformation, and analytics on FHIR® data. A new emerging technique to make queries on FHIR server is SQL on FHIR®  [28] , aiming to have common SQL representation on FHIR® resources, in order to share easily SQL query artifacts between stakeholders. Some analytics tools created their own FHIR® querying language or method; for instance Power BI uses Power Query for this purpose  [29]. From another perspective, HL7® defined a suite of standards  [30, 31]  to normalize quality measurement and querying to FHIR® server, supported by multiple reference implementations  [26].
The resources used to share CQL logic are the Library and the Measure resources. A set of open-source tools are provided by the community  [54]  in order to simplify compiling CQL scripts into the Library and the Measure resources. Many tools and projects exist to compile the Library resources into MeasureReport resources, like for example through a HAPI FHIR® server  [55, 56]  or through LinuxForHealth FHIR Server  [57]. CQL, Clinical Reasoning resources, and the different open-source tools simplified quality measurements on FHIR® servers, by providing the needed methodology and tools to perform clinical reasoning through FHIR® resources. Such technology is very helpful for analytics on radiation data, and can be further applied on FHIR® resources following the radiation dose summary implementation guide.
Multiple open-source tools exist as CQL reference implementations and can be leveraged to construct DRL computation environment through CQL constraints definition. The most known CQL processor by the authors is CQF-Ruler  [59] , which is an implementation of the clinical FHIR® reasoning module, working as a knowledge repository, as well as a FHIR® server. CQF-Ruler can perform quality measure processing. Fig. 6  is a summary on how to exploit CQF-Ruler as an interpreter of DRL constraints. The DMS shares the FHIR® resources to a FHIR® server, configured with the components coming from CQF-Ruler project. DRL thresholds are configured using CQL, Library resources, and Measure resources. The DRL measurements are performed through calling the method $evaluate-measure of the FHIR® server. Another possibility to process the CQL definitions describing DRL thresholds is to follow the method described in Quality Measure Implementation Guide  [31].
Dose analytics with FHIR® and Tableau We have tested the first method presented in the paragraph "Dose Analytics through FHIR® API and Analytics Tool", with a generic analytics tool called Tableau (Salesforce Inc, Seattle, WA)  [61] [62] [63] , but the application can be performed the same way with many other BI tools. Tableau is a powerful data visualization tool used for business intelligence, data analysis, data blending, and works well with both small size and large size of data. It has custom desktop tools allowing to customize reporting and analysis of data. It allows creating workbooks, used to visualize dashboards to end users. Tableau has two main applications, Tableau Desktop and Tableau Server, where the latter can be used to share workbooks created through Tableau Desktop. Tableau brings multiple connectors for data collection, like database connectors (MySQL, PostgreSQL, Oracle, etc.
), excel connector, PDF, textual data, etc. For our use case, Tableau has a specific HTTP connector called "Web Data Connector" (WDC)  [64]. The WDC is a powerful tool provided by Tableau that can be used to extract data from REST endpoints, by making HTTP queries, and parsing their responses by aggregating data in tabular format, through JavaScript methods. The WDC is used in our use case to connect to the radiation FHIR® server endpoint, to collect the metrics and cohorts' values as tabular data, for Tableau, as described in Fig. 7. Connecting the WDC to a FHIR® server is a technique already experimented in the literature  [47]. Using a similar technique allows to map radiation data from the dose management system to Tableau using a standardized API and resources. Tableau was configured to expose the cohorts of data and the radiation metrics to the end user through dashboards and diagrams.
French DRL thresholds with CQL and FHIR® The method described in the paragraph "DRL definition through CQL and FHIR® measurements" was successfully experimented through the French DRLs defined in publication  [65]   (Doroszczuk et al. , 2019). The diagram described in the exposed method was deployed with CQL rules, to collect radiation observations not fitting the conditions and thresholds defined by the French DRLs. We experienced only some reference levels, defined by the "Authorité de Surreté nucléaire"; but most of the conditions needed to write a complete library of CQL definition of the DRLs are achievable through the extracted FHIR® resources, and following the implementation guide radiation dose summary  [15]. The creation of CQL, Library resources, and Measure resources was done using Visual Studio Code, with the plugin Clinical Quality Language (CQL) for VSCode  [66].
The first method described radiation data collection from DMS providers through FHIR® API and exposing these data through diagrams and dashboards to end users using analytics tools. Collected radiation data were divided into metrics and cohorts; each metric can be linked to multiple cohorts. This method was tested using Tableau analytics tool. Many dashboards were created using the collected data, and some examples were provided above, allowing macroscopic visualization of radiation data through different cohorts' levels. Testing of this method with Tableau analytics tool proved its efficiency and its applicability. The collection of radiation data through FHIR® was agnostic to the DMS providers and can be deployed as an addendum to any DMS product. The second identified method described DRLs definition as computable knowledge artifacts using CQL language.
Patients It was a single-center observational study conducted from January 2013 to June 2020. The study was approved by the relevant Institutional Review Board (2211250v0 23/01/2019), (granting a waiver of informed consent given the retrospective nature of study). Two hundred and one patients with histopathologically proven solitary-MET, GBM or PCNSL were consecutively recruited and evaluated. Twenty-one patients were excluded because of artifacts on perfusion data (6 patients), uninterpretable spectral data (4 patients), immunocompromised condition (2 patients), and missing data (9 patients). Thus, we included 180 patients (81 women, 99 men, mean age: 65. 4 ± 15. 7); 77 previously untreated immunocompetent patients with PCNSL, 45 with GBM, and 58 with solitary metastases (Table  1 ).
Post-processing rCBV measurements from DSC MR imaging data were performed using syngo. via software (Siemens). During the first pass of a bolus of contrast agent, T2*-weighted signal intensity decreased. The change in the relaxation rate (DR2*, ie, the change in the reciprocal of T2*) can be calculated from the signal intensity as follows: DR2*(t) ={-ln[S(t)/S 0 ]}/TE, where S(t) is the signal intensity at time t, and S 0 , the unenhanced signal intensity. DR2* is proportional to the concentration of contrast agent in the tissue, and CBV is proportional to the area under the curve of DR2*(t), provided there is no recirculation or leakage of contrast agent. In general, these assumptions are violated, but the effects can be reduced by fitting a gamma-variate function to the measured DR2* curve.
PSR measurements were performed using syngo. via software. ROIs were drawn on the grey-scale perfusion maps overlaid on contrast-enhancing tumor on T1-weighted images. An ROI of 30-40 mm 2 was moved within the tumor area to look for the highest and lowest recoveries on T2*-weighted signal-intensity curves and was selected for maximum and minimum PSR, respectively. For normalization, ROI of approximately 30-50 mm 2 was also placed in the symmetrically contralateral parenchyma, and ratios were obtained. The PSR was calculated as described by Cha et al. (15): PSR = 100% x (S 1 -S min )/(S 0 -S min ), where S 1 , S 0 , and S min are postcontrast, pre-contrast and minimum T2*-weighted signal intensities, respectively. 1H-MR spectroscopy raw data were analyzed with jMRUI (Java-based Magnetic Resonance User Interface) post-processing software.
Signal intensity of each metabolite was obtained using the AMARES algorithm (Advanced Method for Accurate, Robust and Efficient Spectral fitting of MRS data with use of prior knowledge) ), Lip (0. 9ppm) at short TE (35ms), and their ratio were calculated Cho/NAA, Cho/Cr Lac/ Cr, Lip/Cr. The data were normalized using the contralateral Cr resonance signal from the symmetric healthy parenchyma. Spectral quality was examined for each subject based on the following parameters: fullwidth at half maximum (FWHM) as an estimation of the line width of the in vivo spectrum (values in ppm were converted into Hz); SNR as determined by the maximum signal and the residuals of the fitted spectrum; and peak coalescing. The subjects were excluded from the study when there were major alterations of spectral quality.
The structural image-processing tool FSL (http:// www. fmrib. ox. ax. uk/fsl) was used to estimate the gray matter, white matter and CSF content of each voxel and to correct the partial volume effects on the metabolite data. Brain tissue images were extracted by removing the outer skull and scalp surfaces using the FSL Brain Extraction Tool (http://fsl. fmrib. ox. ac. uk/fsl/fslwiki/ BET). Finally, the FAST/FIRST tool (http://fsl. fmrib. ox. ac. uk/fsl/ fslwiki/FAST; http://fsl. fmrib. ox. ac. uk/fsl/fslwiki/FIRST) was used to calculate the segmented tissue percentage in the VOI. Coregistration between the spectroscopic VOI and the segmented image was performed with a user-developed Matlab program (MathWorks, Natick, Massachusetts).
Statistical analysis The variables assessed were spectroscopic concentration ratios (Lac/Cr, Cho/NAA, Cho/Cr, and Lip/Cr), perfusion parameters (rCBVmax, PSRmax), and oncotype groups (METs, GBMs, PCNSLs). Mean values and frequencies were expressed with their standard deviations (± SDs) and percentages (%), respectively. Kolmogorov-Smirnov test confirmed normal distribution of continuous variables. Differences among groups were tested using 1-way ANOVA and the Fisher PLSD post hoc test (Fisher's Protected Least Significant Difference). Data mining algorithms, especially decision-tree, works with no missing data. Therefore, after cleaning and preparing the dataset, only 180 patients were included in the final data analysis. All the variables were included at the same time as input for the Classification and Regression Tree models.
The K fold random cross-validation procedure to train and test classifiers was used with K = 5 to improve the estimated performance of the model, although it is computationally expensive to train the model on multiple training sets. There is a bias-variance trade-off associated with the choice of k in k-fold cross-validation. Typically using k = 5 yields test error rate estimations that suffer neither from excessively high bias error nor from very high variance error. K-fold cross validation randomly splits the dataset into k stratified folds. Iteratively, each of the k sets is used as a test set once from new unseen data to validate the model while the remaining data (k-1 folds) are used as a training set to fit and generate the model. The process is repeated K times, i. e. , as many times as the number of stratified folds.
The ROC plot is a method to visualize and select classifiers based on their classification performance as quantified by the area under the ROC curve (AUC). The classifier's area under the curve (AUC) can be described as the classifier's probability of classifying a randomly selected positive result with the highest predictive accuracy. Statistics were performed using SAS software (version 9. 4; SAS Institute, Carry, NC). A two-tailed P-value< 0. 05 was considered statistically significant.
This is a clear advantage of decision-tree models compared to multivariate regression models. Indeed, decision-tree models reveal the classifiers that have the highest classification accuracy, and yield threshold values of these classifier where tree branching takes place. One key feature is that the developed decision-tree models in this study provide a hierarchical organization of the different classifiers in brain tumor multiclass classification. The most sensitive classifiers appear first in the decision tree and the most specific classifiers last. This allows a hierarchical utilization of the different classifiers for multiclass classification decision making. Moreover, the decision-tree model yields a supervised machine learning classification that can accurately discriminate classifiers based on training dataset, retrospectively acquired, and can assesse the generalization of classifiers on a testing prospective dataset.
The strengths and limitations of the study Our study has several potential strenghs including histopathologic examination to confirm the diagnosis in all patients prior to inclusion, a multiclass classification procedure (three-class classification), K-fold random cross-validation to minimize the risk of overfitting (high variance error and low bias error) and provide a better approximation of the performance of the trained model in clinical practice. The interesting property of a machine learning model is its ability to categorize new unseen data. Cross validation is a technique used to determine how the results of a machine learning model could be generalized to new, unseen data. This approach provides a mechanism to get the test-data Mean Square Error with the current dataset without the need of finding new data to test the model.
Further investigations revealed impaired TRESK channel function associated with this variant  [101]. In another study, pharmacological inhibition of TRESK influenced the response to capsaicin, a TRPV1 receptor agonist, and resulted in increased CGRP release and meningeal blood flow  [102]. These data suggested that MwA can be caused by a rare genetic variant inducing a major functional effect. A study of 200,000 exome-sequenced UK Biobank participants conducted in 2022 found the frameshift variant p. (F139Wfs*24) in 196 (0. 10%) of the 193,433 participants classified as controls and in 10 (0. 14%) of the 7194 migraine cases (p = 0. 33)  [103]. The authors concluded that KCNK18 should no longer be regarded as being involved in migraine etiology. A major limitation of this study is that the clinical status of healthy controls was not assessed in details.