dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Transfer Learning (TL) is a Machine Learning (ML) approach where a trained model for a specific task/dataset is retrained for another task/dataset. In healthcare, TL is currently mainly used for prediction models built upon image data  [1]. Recently, TL is introduced also for Adverse Drug Reaction (ADR) prediction  [2]. Our study aims to develop a TL predictive approach upon Individual Case Safety reports (ICSRs) and Electronic Health Records (EHR), applied for the early detection of ADRs signals, typically elaborated through disproportionality analysis applied in Pharmacovigilance (PV) pipelines. The presented approach is evaluated using alopecia, an ADR observed following chemotherapy with docetaxel in BC patients. An early detection of this ADR could help BC patients to confront anxiety and distress via proper psychological preparation and other potential mitigation measures (e.
Methods ICSR data from the FDA Adverse Event Reporting System (FAERS) will be used for the initial training step and the retrained step will be applied upon EHR data. Finally, the trained TF model will be evaluated in terms of predicting the adverse effect of alopecia on BC patients or the identification of the respective PV signal using the combination of ICSR and EHR data, compared to the use of tradition PV methods. ICSR data are preprocessed, i. e. "cleaned" to facilitate further processing. Relevant information provided in ICSR data is taken into account, e. g. age and suspected drugs. Indicatively, Figure  2  depicts the number of concomitant suspected active substances in the selected ICSRs. Furthermore, ATC codes and the relevant classification of active substances is used to identify potential associations for drug classes.
UV-Vis absorption spectra were recorded at the Faculté des Sciences de Saint-Jérôme (Marseille) with a Jasco V670 instrument equipped with a Peltier cell holder ETCS-761 to maintain the temperature at 20. 0 • C. A quartz cell of 1 mm of optical path length was used. Solution of compound 3 with a concentration of 0. 16 g. L -1 was prepared in acetonitrile (HPLC grade). The UV-vis absorption spectra were recorded using the solvent as a reference and are presented without smoothing and further data processing. The measurement range was 200-800 nm with a data interval of 2 nm, a scan speed of 400 nm/min and a UV-vis bandwidth of 2 nm. HRMS spectrum (ESI) was recorded on a SYNAPT G2 HDMS (Waters) at the Faculté des Sciences de Saint-Jérôme (Marseille). NMR spectra were recorded on a Bruker Avance 200 MHz or a Bruker Avance NEO 400 MHz NanoBay spectrometer at the Faculté de Pharmacie of Marseille.
Acknowledgments: We want to thank  Vincent Remusat  ( Institut de Chimie Radicalaire, Marseille ) for his help with NMR analysis,  Valérie Monnier  and  Gaëlle Hisler  ( Spectropole, Marseille ) for performing HRMS analysis,  Jean-Valère Naubron  and  Sara Chentouf  for IR and UV--Vis spectra.
Background: In May 2020, the ACCESS (The vACCine covid-19 monitoring readinESS) project was launched to prepare real-world monitoring of COVID-19 vaccines. Within this project, this study aimed to generate background incidence rates of 41 adverse events of special interest (AESI) to contextualize potential safety signals detected following administration of COVID-19 vaccines. Methods: A dynamic cohort study was conducted using a distributed data network of 10 healthcare databases from 7 European countries (Italy, Spain, Denmark, The Netherlands, Germany, France and United Kingdom) over the period 2017 to 2020. A common protocol (EUPAS37273), common data model, and common analytics programs were applied for syntactic, semantic and analytical harmonization. Incidence rates (IR) for each AESI and each database were calculated by age and sex by dividing the number of incident cases by the total person-time at risk.
with thrombocytopenia (TP) and mixed venous and arterial thrombosis with TP, respectively. Interpretation: Given the nature of the AESIs and the setting (general practitioners or hospital-based databases or both), background rates from databases that show the highest level of completeness (primary care and specialist care) should be preferred, others can be used for sensitivity. The study was designed to ensure representativeness to the European population and generalizability of the background incidence rates. Funding: The project has received support from the European Medicines Agency under the Framework service contract nr EMA/2018/28/PE. Ó 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4. 0/).
Study design and setting A multi-database dynamic cohort study was conducted in 10 healthcare databases from 7 European countries: Italy, Spain, Den-mark, Netherlands, Germany, France and United Kingdom (UK). The study protocol (EUPAS37273) is publicly available on the European Network of Centers for Pharmacoepidemiology and Pharmacovigilance (ENCePP) register  [17]. The study was conducted over the period 2017 to 2020, except for two databases in which the study ran over the years 2010-2013 for Danish registries (DCE-AU) and 2014-2017 for German Pharmacoepidemiological Research Database (GePaRD). The 10 population-based healthcare databases included data from ARS, PEDIANET (Italy), FISABIO, BIFAP and SIDIAP (Spain), PHARMO (Netherlands), CPRD (UK), GePaRD (Germany), SNDS (France) and Danish Registries.
The databases differed in terms of population size, provenance of the diagnosis (e. g. , emergency room, in and/or outpatient, specialist or general practitioners (GP)) and diagnostic coding systems (International Classification of Diseases (ICD), Ninth Revision, Clinical Modification (ICD-9-CM), and ICD, Tenth Revision, Clinical Modification (ICD-10-CM), ICD-10 German Modification (ICD10-GM), CIM10 (Classification Internationale des Maladies), Read, SNOMED CT US Edition and Spanish Edition (SCTSPA)). Table  1  provides a summary of the main characteristics of the data sources. For three of them (BIFAP, SIDIAP and PHARMO), subpopulations were defined which included individuals with both primary care and hospital medical records (BIFAP_PC_HOSP, SIDIAP_PC_HOSP and PHARMO versus BIFAP_PC, SIDIAP_PC and PHARMO_PC_HOSP).
Study population The source population comprised all individuals observed in one of the participating databases for at least one day during the study period and who had at least one year of data availability before study entry, except for individuals with data available since birth. Individuals were included in the study according to predefined inclusion and exclusion criteria. Reasons for exclusion were: invalid or missing birth date or missing sex record, exit before study entry (01 January 2017; 01 January 2010 for DCE-AU; 01 January 2014 for GePaRD), and less than one year of lookback period prior to study entry. Individuals at increased risk of severe COVID-19 disease were identified according to the presence of at least one of the following underlying conditions in the lookback period or during the study follow-up: cardiovascular disease, cancer, chronic lung disease, HIV, chronic kidney disease, type 2 diabetes, severe obesity (BMI 30), sickle cell disease or use of immunosuppressants.
Data management workflow and data analysis This study was conducted in a distributed manner using a common protocol, a ConcePTION common data model (CDM)  [19]  for syntactic harmonization, a common analytics program for semantic harmonization and data transformation/analysis  [20]. Each data access provider (DAP) applied the Extract-Transform-Load process which led to a syntactic harmonization. The syntactic foundation transforms the structure of the data sets held by each DAP to a common format. To create the study variables semantic harmonization was needed to reconcile differences across different terminologies. A shared semantic foundation was built for each AESI by using a standardized event definition form. For each AESI and underlying condition, medical code lists have been created using the ADVANCE code mapper tool  [21]  and integrated coding systems: ICD-9-CM, ICD-10-CM/GM, CIM10, READv2, SNOMED CT US Edition and SCTSPA.
DAPs were asked to review and update the proposed medical codes based on local coding habits and prior experience. Narrow and broad algorithms were established for most AESIs allowing, respectively, for a specific and a sensitive clinical case definition. Event definition forms including medical code lists were made publicly available through the VAC4EU Zenodo community (https://www. zenodo. org/communities/vac4eu/?page = 1&size = 20). R scripts that included semantic harmonization and transformation of data in the CDM into incidence rates were coded in R using version 3. 1. 0 and distributed to the DAPs for local deployment. Aggregated data were uploaded by each DAP on the Digital Research Environment (https://www. andrea-cloud. eu/azure-dre), a secured Microsoft Azure cloudbased research environment, for final analysis and pooling.
Demographic characteristics including age and person-time of follow-up were computed in each data source. Incidence rates (IR) and 95 % exact confidence interval (95 %CI) for each AESI and for each database were calculated for the study period, by year, age and sex and by dividing the number of incident cases by the total person-time at risk. Age-standardized IRs (according to the European population  [22] ) for the period 2017-2019 (or 2010-2013 for DCE-AU or 2014-2017 for GePaRD) were pooled using the DerSimonian and Laird meta-analytic approach for random effects models according to the provenance of the events (Table  1 ). Incidence rates were expressed per 100,000 person-years (PY). Percentage change between the years 2017-2019 versus 2020 were also computed to assess the change in health care utilization during the COVID-19 pandemic.
Characteristics of the population A total number of 63,456,074 individuals were included in the study, contributing to 211. 7 million person-years. Demographic characteristics are available in Table  2. The largest contributions in person-time were from GePaRD (17. 1 %) and BIFAP (16. 3 %), followed by SNDS (13. 7 %), PHARMO (12. 6 %), Danish registries (10. 6 %), FISABIO (9. 9 %), SIDIAP (9. 4 %), CPRD (6. 0 %), ARS (4. 3 %) and PEDIANET (0. 2 %). Subpopulation sizes of data sources with both GP and hospital diagnoses were available (through linkage for part of the population) for 100 %, 43. 1 %, 28. 3 % and 5. 4 % of the full included population for FISABIO, BIFAP, SIDIAP and PHARMO, respectively. The study flowchart is available in Supplementary materials (Fig. 1 ). The proportion of individuals affected by conditions which increase the severity of COVID-19 varied across databases, cardiovascular diseases and chronic lung diseases were the most prevalent risk factor for serious COVID-19 (Fig.
Incidence rates of AESIs IRs per database, per year, age and sex are detailed in the final study report available on Zenodo website  [23]  and on the VAC4EU dashboard  [24]. Incidence rates that are presented in this paper used the narrow clinical definitions and are for time periods that exclude the year 2020. Table  3  presents age-standardized pooled incidence rates for all AESIs according to the provenance of events over the study period. Plots in Fig. 1  depict the incidence rates per age and according to the provenance of events. Age-and sexstratified incidence rates for all AESIs according to the ACCESS recommendations are presented in Supplementary materials (Table  1 ). For the autoimmune diseases, pooled IRs for ADEM, GBS and narcolepsy were the lowest rates. Provenance of diagnoses impacted substantially the observed rates with diagnoses of narcolepsy, GBS and diabetes most frequently reported in settings including in-outpatient and/or GPs records.
A clear age and sexpattern was shown for GBS and TP. IRs for GBS and TP were slightly elevated in males. Cardiovascular disorders were more frequently reported in the hospital setting. Microangiopathy and stress cardiomyopathy showed the lowest rates compared to other cardiovascular disorders. A peak of myocarditis and myocarditis/ pericarditis was observed in the 20-29 age category and higher rates of SOCV were observed in the younger population (0-19). IRs for myocarditis and coronary artery disease were higher in males, while IR for stress cardiomyopathy was higher in females. For circulatory disorders, IRs were higher for diagnoses from hospital records as compared to diagnoses in GPs records. Rates ranged from 229. 67/100,000 PY for venous thromboembolism (VTE) to 0. 85/100,000 PY for CVST in databases including GPs and hospital medical records.
Discussion Based on data from 63 million European individuals, this cohort study generated age-and-sex specific background incidence with high precision for a pre-specified list of 41 AESIs, necessary for monitoring the safety of COVID-19 vaccines. We generated background incidence rates using a distributed data network with common protocol, common data model and common analytics using 10 diverse healthcare databases across 7 European countries. These rates have been reported from January 2021 onwards, periodically and were used throughout 2021 by the European Medicines Agency and vaccine manufacturers for observed/expected analyses (personal communication). Gubernot et al. (2021)  [25]  recently conducted a literature review of incidence rates of 22 AESIs, as well as the Brighton Collaboration (9 events); our overall rates are consistent with the literature derived data although we could not compare age and sex strata.
Li et al. (2021)   [26]  recently published a study on AESI incidence rates from the OHDSI network, which covered general practice or claims data from eight countries (USA, UK, Australia, France, Germany, Spain, Netherlands and Japan) and reported on 15 AESIs. In general, our results differed substantially for several of the 12 common AESIs, which may be explained by the fact that incidence rates from the 8 countries were pooled regardless that the provenance of the events that went into the numerator differed substantially: 5 of the OHDSI data sources only captured GP recorded diagnosis data, whereas US and Japanese data captured claims. Our approach and strength were to pool results only across similar provenance of the event and to present the rates by provenance, independently. We considered the different provenances in the analysis as this is crucial for the correct interpretation of realworld evidence derived from heterogeneous data sources, several AESIs are only diagnosed in secondary care and are underestimated in primary care medical records, such as cardiovascular and thrombotic events.
We would recommend that rates are presented by provenance and that this diversity is preserved in the pooling for the observed/expected analyses. To give examples we briefly describe and compare the rates for selected AESIs with published data focusing on AESI that have been identified as safety risks following administration of COVID-19 vaccines. More detailed contextualization of the rates for each of the different AESI against published references is available in our final study report  [23]. Our VTE rates (pulmonary embolism and deep vein thrombosis) were of similar magnitude compared to literature data retrieved by  Gubernot et   [28] , with a notable increase of incidence with age. For TTS, we operationalized the Brighton Collaboration case definition after a public webinar by VAC4EU & Brighton Collaboration (https://youtu.
In this study, age, sex-stratified and comorbidity specific rates were generated from 10 existing large electronic healthcare databases in 7 European countries, with semantically harmonized data. Because each data source has its own characteristics with regards to provenance of the events (GPs only, in or outpatient settings, emergency room visit or specialist referrals), we provided pooled estimates according to the provenance of the events. Background rates can be generated prior to vaccination roll-out when electronic health data are available, but the users should be aware of the nature of the event, the setting in which it is diagnosed, and evaluate whether the data source appropriately captures the data. Data sources that contain data from the setting where the disease is typically diagnosed should be preferred.
In this study, given the Table  3  Pooled incidence rates and 95% confidence intervals for all AESIs (narrow definition) over the study period* according to the provenance of the events databases. nature of the AESIs included in this study, we recommend using background rates from data banks that show the highest level of completeness of identification of these events in terms of the type of diagnoses (such as GP and hospital-based data sources) which includes all data sources with such subpopulation. For some events, such as CVST, data sources including emergency and outpatient visits may be preferred, while for anosmia-ageusia or chilblain-like lesions, data sources including GPs setting would be recommended (see Table  1  in Supplementary materials).
Strengths and Limitations ACCESS was a project funded by the EMA to prepare European infrastructure to monitor COVID-19 vaccines. The project ran from May 2020 to July 2021 and delivered background rates of AESIs, template protocols for implementation of observational studies, and feasibility assessments in each country to participate in studies and analyses performed by EMA for its Scientific Committees and ECDC. All deliverables have been made publicly available immediately to the scientific community through the EU PAS register (https://www. encepp. eu/encepp/studiesDatabase. jsp), the VAC4EU website (https://vac4eu. org) and Zenodo (https://www. zenodo. org). European data sources are quite heterogeneous because of different coding systems, health care practices, provenance of diagnosis and systems.
Conclusion The ACCESS project started at an early stage of the COVID-19 pandemic as a component of the EMA readiness strategy for the times where vaccines would be authorized. ACCESS was successful in delivering these data on time as the first set of background rates were made available to EMA in December 2020, providing support to the safety monitoring of vaccines as soon as they were available in the EU. A large population of 63 million European individuals was included in the study, without restrictions beyond study period, to ensure representativeness to the European population and generalizability of the background incidence rates.
Mar Martín-Pérez, Patricia García-Poza, Airam de Burgos, Maria Martínez-Gon zález and Verónica Bryant are employees at the Spanish Agency for Medicines and Medical Devices which fully finance the BIFAP database, a non-profit database for performing independent pharmacoepidemiologic research in the public research Spanish setting. Consuelo Huerta was employee at the Spanish Agency for Medicines during the time in which this study was performed. She is currently assistant professor of the Public Health Department of the Faculty of Medicine in the Complutense University of Madrid. Ulrike Haug and Tania Schink are working at an independent, non-profit research institute, the Leibniz Institute for Prevention Research and Epidemiology -BIPS. Unrelated to this study, BIPS occasionally conducts studies financed by the pharmaceutical industry.
Acknowledgement The research leading to these results was conducted as part of the activities of the EU PE&PV (Pharmacoepidemiology and Pharmacovigilance) Research Network which is a public academic partnership coordinated by the Utrecht University, The Netherlands. EU PE&PV collaborated with the Vaccine Monitoring Collaboration for Europe (VAC4EU) association The project has received support from the European Medicines Agency under the Framework service contract nr EMA/2018/28/PE. The content of this paper expresses the opinion of the authors and may not be understood or quoted as being made on behalf of or reflecting the position of the European Medicines Agency or one of its committees or working parties.
Optimization of formulation In optimization process Central Composite Statistical Design was employed to optimize the nexus by using Design Expert Ò software (version 13. 0. 5. 0. Stat-Ease. , Minneapolis, MN, USA). In this design 4 factors and 2 levels of each variable (-1, 0 and + 1) was employed to studied the impact of independent variable (ratio of polymer, monomer and crosslinker) on dependent variable like swelling and drug release. Observed response was recorded. All experiment were conducted in triplicate and results were presented as ± SD. Dried interpenetrating network discs were cut or crushed into small pieces and their weight (Wo) was noted by using a digital weight balance. Weighed interpenetrating networks disc pieces were placed into Soxhlet extractor at 100 °C with deionized water for a period of 24 h.
4. Release results were predicted via RSM graph and contour plot as shown in Fig. 12. Central Composite design was employed to investigate the release profile. Release was evaluated by using two independent variables (pH 1. 2 and 7. 4). The response surface was evaluated via Design Expert software by using coded values of factor values. The regression model was remarkable, and significantly valid for a considered response as indicated by P values (P < 0. 05), R1 and coefficient of variation. These types of plots are used to employ the influence of two factors on the response at one time, while the third factor was kept constant. In formulation IPN1-IPN3 linear relationship was observed and swelling decreased with increase in MBA concentration. However, nonlinear and curvilinear relationship of factor X with factor Y was observed for formulation IPN4-IPN6 (Varying Natrosol concentration) and IPN7-IPN9 (varying Pectin concentration).
Evaluation of Tapentadol Hydrochloride release kinetics Kinetic modeling was applied on Tapentadol Hydrochloride release data by using DDS solver software in order to find out release pattern from Natrosol-Pectin IPN formulations and best fit model. Models such as First order, Zero order, Korsemeyer-Peppas and Higuchi model were applied for obtaining R 2 values of all developed formulations. It was clearly evident from R 2 values that Tapentadol Hydrochloride release from HEC-Pectin IPNs followed Zero order kinetics. While the Korsemeyer-Peppas model revealed mechanism of drug release to be Super case type II transport as value of 'n' was greater than 0. 89.
The dissolution profiling of MTX-LSMCSNPs was carried out by applying these models via DDsolver software to ensure that the drug dissolution from the prepared formulations was occurring in an appropriate manner  (Mircioiu et al. , 2019). Zero Order Kinetics: This model highlights that MTX-LSMCSNPs were at a concentration-independent release rate. The zero order kinetics is represented in following equation. Q t ¼ Q o þ K o t Where Q t signifies the amount of drug dissolved in time t, Q 0 the initial drug concentration in the solution, and K 0 the zero-order release constant  (Libo and Reza 1996, Freitas and Marchetti 2005). First-order kinetics: The model was used to explore the hydrophilic drugs release from a porous matrix and compared the drug content inside the drug carrier as represented as below.
Statistical analysis Every test and procedure was repeated in triplicate manner and presented as mean ± S. D. The obtained data were statistically analyzed by one-way ANOVA, p less than 0. 05 alongside a Tukey posthoc test to evaluate the significant variations, if presented between independent variables. Graph-Pad Prism 8. 2 was used for the analysis of data and making graphs. DDSolver software was used to generate R 2 and MSC values to validate the accuracy and prediction capabilities of several kinetic models.