dataesr/scientific-paragraphs-categorization huggingface dataset:
Dipeptidyl peptidase-4 (DPP-IV) inhibitors are known as safe and well-tolerated antidiabetic medicine. Therefore, the aim of the present work was to synthesize some carbohydrazide derivatives (1a-5d) as DPP-IV inhibitors. In addition, this work involves simulations using molecular docking, ADMET analysis, and Lipinski and Veber's guidelines. Wet-lab synthesis was used to make derivatives that met all requirements, and then FTIR, NMR, and mass spectrometry were used to confirm the structures and perform biological assays. In this context, in vitro enzymatic and in vivo antidiabetic activity evaluations were carried out. None of the molecules had broken the majority of the drug-likeness rules. Furthermore, these molecules were put through additional screening using molecular docking. In molecular docking experiments (PDB ID: 2P8S), many molecules displayed more potent interactions than native ligands, exhibiting more hydrogen bonds, especially those with chloro-or fluoro substitutions.
Molecular Docking The molecular docking was performed using Autodock Vina 1. 1. 2 in PyRx 0. 8  [24]. The process of ligands and protein purification is described in our previously published paper  [25]. The Universal Force Field (UFF) has been assigned to carry out energy reduction and optimization  [26]. Figure  3  depicts the structure of the DPP-IV enzyme (PDB ID: 2P8S) acquired from the PDB site. The enzyme structures were refined, purified, and prepared for molecular docking using Discovery Studio Visualizer 2019  [27]. The whole molecular docking approach, including identifying cavities and active amino acid residues, was carried out as reported by Khan et al. [15, [28] [29] [30] [31] [32].
Others, such as temporal/causal shifts in the distribution even imply that any individual result would be different at the current time than it was at the time of the study, implying that some studies should come with an expiration date. A possible source for these biases is the way the annotations themselves are generated as the datasets generally only contain data where it is feasible to have annotations. In addition, this level of feasibility and accuracy change over time with different annotation and visualization tools  [7]  as well as being dependant on the experience of the annotators themselves  [8]. Additionally, annotations can introduce other biases due to systematic biases in the annotations themselves, such as prioritizing smoothness only in the inplane directions due to disagreement between a single user's segmentation across consecutive slices.
To give an example, several medical image computing datasets include multiple images of the same patient across different time points or even just twodimensional slices from the same three-dimensional volume. (One reason why these types of errors are so insidious is the complexity of some evaluation methods, such as cross-validation and nested cross-validation, where the difference between a correct and a leaking implementation can be difficult to detect for both authors and reviewers. ) There are obvious correlations here that could be leaked if images from the same patient found themselves in both the training and the evaluation datasets at the same time. However, this is also the case with particular hospital centers. Should multi-center experiments require all of the images from each center to fall on the same side of the training/evaluation divide? In our field of study, there is no simple answer to these questions as bias is not a matter of type but of degree.
Similarly, for algorithms adjacent to the primary machine learning task, (such as data normalization or detecting invalid input,) some small amounts of leakage might have negligible detrimental effects, but also non-negligible positive effects. For example, examining the entire dataset to see what constitutes an artifact or a missing value (e. g. is a default value used? a NaN?) is technically leakage as you would also be examining the evaluation dataset. However, the positive gain from this (i. e. the removal of elements that would otherwise break the algorithm or the design of non-intelligent methods to detect these invalid inputs in the clinic) would be immense, although this is a manner Table  1. Some examples of methodology-centric elements of bias and their definitions.
Distribution biases Unrepresentativeness: when the distribution of the evaluation data used in the paper differs from that observed in the clinic, due to the influence of human-centric elements of bias or other (possibly unknown) factors Data selection bias: when the dataset itself captures only a portion of the relevant distribution due to experimental design, technology, or feasibility reasons Annotation bias: when the annotations themselves include a consistent bias or source of error, such as simplification or differences between the inplane and out-of-plane quality Temporal/causal shift: when the distribution itself changes over time, possibly due to the influence of the system under investigation Methodology selection biases Model selection bias: when the model with the highest measured performance is selected from a group of measured models and said measurement performance is directly reported (i.
e. no independent evaluation) Metric/ranking selection bias: differences between the metrics used to evaluate a framework and the underlying ill-defined notion of clinical quality (an example of this is how one deals with class imbalance) Data leakage Feature leakage: when certain features are provided as input to an algorithm when they would be unavailable in practice temporal leakage: when information that is normally not available until after the machine learning task is provided as input for said task correlation leakage: when a feature is irrelevantly correlated with the prediction in the experiment but not in intended use Train/test leakage: information from the evaluation (i. e. testing) dataset is used at least partially to develop the models which are then evaluated on said dataset normalization/imputation/augmentation leakage: when knowledge of the evaluation dataset is used to develop algorithms besides the primary one stacking/boosting/parameter leakage: when the evaluation set is used in the process of combining or selecting from multiple models or configurations of degree in terms of the methods being implemented and the knowledge is extracted.
One reaction to this would be complete isolation, that researchers identify and remove all possible elements of bias, and this would be a valid response if these biases were a matter of type and not of degree. For example, in some contexts, it may be appropriate to ensure that all data arising from one hospital center is kept entirely separate from another. In others, the bias introduced by mixing centers in the dataset is not only negligible but irrelevant and damaging. For some research, we benefit from researchers building upon others methods using the same open datasets. In others, the improvement garnered reflects community-level overfitting more than real progress. In my opinion, we should be unequivocal in asking authors to more carefully perform experiments and for reviewers to more carefully examine papers, cognizant of different biases.
Participants were age 18 years with unresectable or metastatic BRAF V600mutant melanoma. The primary end point was investigator-assessed progression-free survival. Overall survival was a key secondary end point (ClinicalTrials. gov identifier: NCT02967692 ). RESULTS At data cutoff (July 1, 2020), the median progression-free survival was 16. 2 months (95% CI, 12. 7 to 23. 9 months) in the sparta-DabTram arm versus 12. 0 months (95% CI, 10. 2 to 15. 4 months) in the placebo-DabTram arm (hazard ratio, 0. 82 [95% CI, 0. 66 to 1. 03]; P =. 042 [one-sided; nonsignificant]). The objective response rates were 69% (183 of 267 patients) versus 64% (170 of 265 patients), respectively. Grade 3 treatment-related adverse events occurred in 55% (146 of 267) of patients in the sparta-DabTram arm and 33% (88 of 264) in the placebo-DabTram arm.
INTRODUCTION ] [3] [4] [5]  Despite these advancements, nearly half of the patients with unresectable or metastatic melanoma die within 5 years of starting treatment. [11]  Although part 3 of the phase II KEYNOTE-022 study (ClinicalTrials. gov identifier: NCT02130466) did not meet the primary end point of investigator-assessed progression-free survival (PFS) at initial analysis, a second analysis, with an extended median follow-up of 36. 6 months, demonstrated a numerically higher PFS rate in patients treated with the anti-programmed death receptor 1 (PD-1) antibody pembrolizumab in combination with dabrafenib and trametinib than in patients treated with dabrafenib plus trametinib alone (hazard ratio [HR], 0. 53 [95% CI, 0. 34 to 0. 83]). 12 In addition, the anti-programmed death ligand 1 (PD-L1) antibody atezolizumab in combination with vemurafenib plus cobimetinib has also been evaluated in the phase III IMspire150 study (ClinicalTrials.
gov identifier: NCT02908672). 13 The triple combination demonstrated a significantly improved median PFS of 15. 1 months compared with 10. 6 months with vemurafenib plus cobimetinib alone (HR, 0. 78 [95% CI, 0. 63 to 0. 97]; P 5. 0249 [two-sided]). These placebocontrolled studies provide clinical evidence of first-line treatment efficacy with checkpoint inhibitor plus targeted therapy combination. COMBI-i (ClinicalTrials. gov identifier: NCT02967692)i sa global, randomized, phase III trial evaluating the safety and efficacy of the anti-PD-1 antibody spartalizumab in combination with dabrafenib and trametinib (sparta-DabTram). 14, 15 Findings from the open-label parts 1 (safety run-in; n 5 9) and 2 (biomarker cohort; n 5 27) showed the treatment regimen to have an acceptable safety profile and promising efficacy.
Study Design and Participants COMBI-i (ClinicalTrials. gov identifier: NCT02967692)i sa global phase III study consisting of three parts: a safety run-in (part 1); a biomarker cohort (part 2); and the randomized, double-blind, placebo-controlled part 3, which was conducted at 179 centers in 29 countries worldwide. The trial was conducted in accordance with the provisions of the Declaration of Helsinki and Good Clinical Practice guidelines. The study Protocol (online only) was approved by the institutional review board or human research ethics committee at each site. All patients provided written informed consent. Part 3 enrolled patients age $ 18 years with histologically confirmed unresectable or metastatic (according to the American Joint Committee on Cancer's Cancer Staging Manual, 7th edition) BRAF V600-mutant cutaneous melanoma.
Random Assignment and Blinding Patients were randomly assigned (1:1) to receive sparta-DabTram (treatment arm) or placebo-DabTram (control arm). Dabrafenib plus trametinib is an internationally approved treatment for patients with BRAF V600-mutant unresectable or metastatic melanoma and was considered an accepted comparator for this patient population at the time of study initiation. 16, 17 Random assignment by investigators or study site staff followed a random permuted block scheme and was conducted using interactive response technology. Treatment identity was blinded from the time of random assignment until the primary analysis database lock. Patients could be unblinded to manage.
CONTEXT Key Objective It was hypothesized that upfront combination of immune checkpoint inhibitors and BRAF plus MEK inhibitor targeted therapies might yield durable responses in more patients with BRAF V600-mutant metastatic melanoma. Thus, we conducted this randomized, double-blind, placebo-controlled, phase III trial (COMBI-i; ClinicalTrials. gov identifier: NCT02967692) to evaluate spartalizumab plus dabrafenib and trametinib (sparta-DabTram) versus placebo plus dabrafenib and trametinib (placebo-DabTram) in this patient population.
Outcomes The primary end point was investigator-assessed PFS (per RECIST 1. 1), defined as the time from random assignment to first documented disease progression or death because of any cause. OS was a key secondary end point. Additional secondary end points included ORR, duration of response (DOR), disease control rate, safety and tolerability, patientreported outcomes, pharmacokinetics, prevalence and incidence of antidrug antibodies, and outcomes on the basis of PD-L1 expression. Adverse events (AEs) were graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events version 4. 03 and reviewed by an independent data monitoring committee. Biomarker analyses are described in the Data Supplement (online only).
Statistical Analysis The primary analysis was planned after the target number of 352 events was observed or when all patients had at least 24 months of follow-up, whichever came first. Rationale and further details are provided in the Data Supplement. Efficacy analyses were performed using the full analysis set of all patients randomly assigned to receive study treatment; safety analyses included all patients who received at least one dose of spartalizumab or placebo, dabrafenib, or trametinib. Kaplan-Meier analysis was used to estimate PFS and OS distributions. HRs were calculated from a stratified Cox model on the basis of the random assignment stratification factors. Significance was determined by a stratified log-rank test at an overall one-sided 2. 5% level.
Treatment with both a checkpoint inhibitor and targeted therapy in the first-line setting would leave few options for subsequent therapeutic lines in patients who experience PD. Instead, because upfront combination of a checkpoint inhibitor plus targeted therapy appears not to be an ideal therapeutic strategy for most patients, continued evaluation of sequencing approaches may be warranted. Targeted therapy and immunotherapy sequencing is under evaluation in several clinical trials (eg, DREAMseq, Clinical-Trials. gov identifier: NCT02224781; ImmunoCobiVem, ClinicalTrials. gov identifier: NCT02902029; EBIN, Clin-icalTrials. gov identifier: NCT03235245; SECOMBIT, Clin-icalTrials. gov identifier: NCT02631447); however, as of this writing, no definitive conclusions have been reached. Preliminary results from the phase II SECOMBIT trial evaluating a targeted therapy-to-immunotherapy switch at PD, an immunotherapy-to-targeted therapy switch at PD, or an 8-week targeted therapy sandwich before immunotherapy suggest that the latter two approaches may be associated with higher 3-year total PFS and OS rates, although these results were not statistically significant.
IV. E-FCR USE FOR R&D ACTIVITIES OF THE TECHNOLOGY ROADMAPS -DEMONSTRATION CASE 5 R&D activities often need wide access to various sources of information, technologies and skills. Some pre-design studies can be greatly facilitated if certain technologies, tools or environments can be made available quickly. Demonstration Case 5 aims to demonstrate the suitable use of the E-FCR and its connected cyber ranges throughout R&D projects. The selected technologies are a SIEM tool for the Maritime sector CyMS), a Penetration Testing tool for attack simulation and detection of vulnerabilities, and a service/device monitoring system (MonSys). CyMS is a software produced by Naval Group and it is a cyber-threat monitoring and an alert tool: it is a kind of Security Information and Event Management systems.
SIEMs are tools designed and developed to gather logs from multiple sources. Based upon third-parties and Naval Group's own components, the CyMS aims to foster the Cyber Defence of IT/OT infrastructure onboard ships. With its two main functionalities: cyber supervision and administration, it enables operator's awareness of relevant cyber threats and suggest mitigation actions to contain the attack. It collects logs from heterogeneous distributed sensors, processes datasets, raises value-added alarms and manages cyber incidents. Human Machine Interface (HMI), which is being developed as a prototype under the scope of the ECHO project, is dedicated to respond to the challenge of the presentation of a complex situation to crew members who are not cyber experts. The E-FCR will contribute efficiently to the development of CyMS by providing network emulation environments where the CyMS can undergo functional tests: this will generally be of assistance to the CyMS developers, able to constantly test their software on a realistic network environment.
 Data normalization In the context of Demonstration Case 5 the bridge could be used to generate and send to interested parties an alert and subsequent sanitized ticket through the E-EWS about discovered relevant service disruptions or anomalies. A thirdparty customer developing a service using the same or similar infrastructure could use this intelligence to request a cyber range to test the applicability of this information within the context of their service. The cyber range providers compile the related infrastructure and testing tools and rent it out to the customer. Such a scenario demonstrates the capability of the MonSys Bridge through the E-EWS and E-FCR to contribute to the secure software development of applications for various industries and use cases across Europe. The E-FCR must allow the owner of the tool to: 1) Decentralized approach:  The bridge is deployed and configured leveraging the use of E-EWS with an external monitoring system.
Trial registration number NCT04321265. Author summary COVID-19 remains a complex disease, making it challenging to estimate the risk of deterioration of critically ill patients and consequently allocation of clinical resources, such as ventilators. As a result, there is a need to support clinical decision making through objective methods and address some of the limitations of the current clinical scoring systems. In response, we developed machine learning models using routine clinical data of patients from 37 countries worldwide, including 18 European countries. We find that: i) machine.
Indeed, a recent review on chest imaging, emphasised the importance of validation dataset to assess generalisability of the model to other cohorts, rather than only on the sampled population  [16]. Therefore, the main objective of this work is to investigate whether the use of routinely collected Electronic Health Records (EHR) data in older patients with COVID-19, coupled with machine learning (ML) algorithms can generalise to diverse patients' populations, to estimate the risk of ICU and 30-day mortality, as well as identify patients at low risk of deterioration, likely to survive without a therapeutic intervention. We hypothesised that a machine learning model derived in a cohort of COVID-19 older patients can be used to predict clinically relevant outcomes of both, geographically and temporally (between pandemic waves) diverse cohorts.
Methods To address our hypothesis, we developed and validated several machine learning models, derived from data collected from 3,933 older patients with a confirmed COVID-19 diagnosis, admitted to ICUs located in 37 countries, between January 11, 2020, and April 27, 2021 as part of the COVIP study (trial registration number NCT04321265,  March 25, 2020). We evaluate the resulting models in a i) retrospective study with validation between the European countries to assess inter-country generalisability of the European model; ii) prospective study between the pandemic waves to evaluate the ability of the model derived from a cohort of patients admitted during a COVID-19 wave to generalise in predicting outcomes in patients admitted to European ICUs during the subsequent wave; and iii) external validation in a cohort of non-European patients, to evaluate whether the model derived from the overall European cohort can be used to predict outcomes in highly diverse patients, such as those admitted to Asian, African and American ICUs.
Study design Our analysis primarily focuses on the European patient cohort, while we used the non-European cohort as the external validation dataset to investigate the generalisability of the models when encountering diverse patient populations, such as those from different continents. The overall workflow and study design is depicted in Fig  1. Initially, we retrospectively assessed the generalisability of the models among the European cohort, evaluating the predictive performance of the models derived from 16 European countries (as shown in Fig  2 ) using the patient cohort from France (that had the highest number of ICU admissions) as the validation. Furthermore, we evaluated inter-country generalisability of the European model, by evaluating its performance on top-nine European countries (based on the highest number of ICU admissions) separately, each time deriving the model from the patient cohorts of the remaining countries, in a leave one country out approach.
Following from this, we prospectively assessed the temporal generalisability of the models between different waves, by deriving a model from a patient cohort admitted before December 1 st , 2020 (our cut-off date) and validating in a cohort of patients admitted on or after December 1 st , 2020. The cut-off date was chosen based on the availability of the data as well as peak of Lastly, we externally evaluated the predictive performance of the models derived from the overall European patient cohort to assess their generalisability in a validation cohort of patients admitted to Asian, African, and American ICUs. For each study design we defined three outcomes of interest, namely ICU mortality, 30-day mortality and identification of low-risk patients. For the internal evaluation of each study design, namely of the European cohort leaving out France, European cohort admitted before the cut-off date December 1st, 2020, and the overall European cohort, we used stratified 5-fold cross-validation with 10 times repetition, starting with different initial random states to mitigate the randomness effects of a single train-test split.
Clinical data sources and settings The study included older patients (over 70 years) admitted to ICUs originating from 37 countries around the world, with a confirmed diagnosis of COVID-19 based on a positive polymerase chain reaction (PCR) test. National coordinators of the study oversaw ICUs recruitment, obtaining national and local ethical approval, and supervising patient recruitment. Ethical approval was mandatory to participate in the study. The study was in line with the European Union General Data Privacy Regulation (GDPR) directive as part of the multi-centre COVIP clinical trial (ID: NCT04321265), where a database was established to facilitate the information sharing of electronic case report forms (eCRF) of each subject on a secure server at the Aarhus University, Denmark.
Study population All the patients involved in this study were at least 70 years old, admitted to 217 different ICUs from 172 cities in 37 independent countries between January 11, 2020, and April 27, 2021. The dataset included overall 3,933 patients with a unique eCRF record since each patient could only be entered into the database once regardless of their transfer to another ICU or readmission. After applying the selection criteria 3,474 patients remained, out of which 2,858 patients were admitted in European ICUs, while 616 patients were admitted in Asian, African and American ICUs. Patients were excluded due to having negative sarscov2 test and significant missing information, as shown in selection criteria diagram in S8 Text. All patients were followed up through a phone interview for their survival status after 30-days and 3-months from the ICU discharge.