dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , end-stage renal disease) but often exclude those under the age of 65 years. To study diseases that span across ages and types of payers, such as infections, it may be necessary to combine findings from multiple sources of RWD with different patient populations (e. g. , commercial claims + Medicare claims + Medicaid claims). Alternatively, researchers can consider an open network claims database that includes all payers (e. g. , IQVIA Rx/Dx and Symphony Health), although these data sources often lack details about patient health insurance coverage status and contain only unadjudicated claims that may later be rejected by payers. A more general limitation for all claims databases is that they rely exclusively on clinical coding (e. g. , International Classification of Diseases (ICD)-10 diagnosis codes) to convey health status, because claims are designed primarily for billing purposes, and such coding may be inaccurate or imprecise to identify or confirm a specific disease of interest.
It can also be difficult to attribute specific healthcare encounters (e. g. , office visit and hospital admission) to a single health condition when multiple diagnoses are present in the claims data. Data from EHRs generally offer richer information about patient demographics (e. g. , race, ethnicity, marital status, and occupation), lifestyle (e. g. , smoking status, use of alcohol, and recreational drugs), medical history (e. g. , vaccinations, family history, and lines of therapy), drug allergies, use of over-the-counter medications, vital signs (e. g. , body mass index (BMI) and blood pressure), symptoms (e. g. , severity, frequency, and duration), examination findings, clinical reasoning (e. g. , differential diagnosis), and treatment plan (e. g. , specialist referral). 67 However, researchers interested in using EHR data should be aware of several limitations.
A general drawback of EHR data is that specific data elements (e. g. , BMI) are often missing and it is unclear if this missingness pattern is random. For example, a study found that BMI was more likely to be recorded in EHR data for patients with type 2 diabetes mellitus (T2DM) than those without T2DM. 68 Because individuals with T2DM generally have a higher BMI, differences in data availability could bias comparisons of BMI between these two groups. EHR data may also contain conflicting or inaccurate information which is exacerbated with repeated measurements (e. g. , large fluctuations in body weight over short time intervals may indicate a data entry error). 69 Additionally, EHR databases are often limited to healthcare providers who use a specific type of EHR system or have a data sharing agreement and may therefore not include all health care received by a patient (e.
g. , EHR data may not include data from allied health providers who use a different EHR system). Data on medication use in EHRs generally reflects medications that are prescribed rather than dispensed and consumed. Although some EHR databases include results of basic laboratory tests (e. g. , complete blood cell counts and metabolic panel), limited information is available about the specific test methods used, and EHR databases generally do not contain detailed results about more advanced diagnostic tests (e. g. , imaging, biopsies, and genotyping). It should also be noted that the unstructured EHR data (i. e. , detailed clinical notes) that may be of greatest interest to researchers often cannot be shared by data vendors due to privacy concerns. Biopharmaceutical companies must therefore rely on EHR data vendors to review, interpret, analyze, and summarize this information on their behalf according to a study protocol and analytical plan; such studies can be very costly and must often be repeated for each disease of interest.
Natural language processing is increasingly used to more efficiently analyze large volumes of unstructured EHR data but such techniques are still evolving. Patient registries (i. e. , disease or product registry), on the other hand, often contain standardized and longitudinal information about patients with specific diseases (e. g. , DMD) or who received specific therapies (e. g. , gene therapy for SCD). 70 These data can provide insights about changes in outcomes (e. g. , patient-reported outcomes and physician assessments) over time and on how patient covariates (e. g. , genotypes, phenotypes, comorbidities, prior therapies, and concomitant medications) can impact such changes. For example, the Collaborative Trajectory Analysis Project (cTAP) combines data from multiple registries related to DMD that contain standardized measures such as the North Star Ambulatory Assessment taken at regular intervals.
71 Data from such patient registries are often used in clinical pharmacology to develop pharmacometric models (e. g. , disease progression model), create external control for single-arm studies or in silico (simulated) trials. Although patient registries focused on specific diseases or therapies may have a much smaller total patient population (e. g. , hundreds or thousands) than those found in large claims or EHR databases (e. g. , tens of millions), they may nevertheless represent the single largest available data source for that disease or therapy. One particular limitation of patient registries is that they lack a comparable control group (i. e. , patients without the disease or therapy of interest); as a result, comparative studies using registry data often combine them with other sources for an appropriate control group.
Given these tradeoffs, when selecting a source of RWD for clinical pharmacology purposes, it may be necessary to combine information from multiple data sources to answer all research questions. However, this process can be challenging for biopharmaceutical companies because RWD are generally de-identified by data vendors prior to being shared. The data vendors (e. g. , IQVIA and Optum) who have access to patient identifiers (e. g. , name, date of birth, and health plan identifier)  must therefore be involved with the process of combining RWD databases. Traditionally, this was accomplished by licensing multiple types of RWD (e. g. , claims and EHR) from one data vendor who would link databases with a common patient identifier prior to de-identifying and sharing the data externally. More recently, patient tokenization, for example, creation of a unique patient identifier that cannot be used to re-identify the patient without proprietary software, offers new options to combine sources of RWD.
For example, companies with tokenization technology (e. g. , Health Verity and Komodo) can now offer access to different types and sources of RWD (e. g. , claims and EHRs) that can be linked by a common patient token prior to de-identification and licensing. As more RWD vendors use tokenization technology, the number and types of databases that can be linked to provide a more comprehensive understanding of patient health will increase. Perhaps more importantly, this same tokenization technology will also allow biopharmaceutical companies and clinical research organizations that generate patient-level data in clinical trials to link trial participants to external sources of RWD, allowing for hybrid study designs (e. g. , clinical trial extensions with RWD). Furthermore, if combing RWD from multiple sources is not possible, another potential alternative is to design a prospective RWD study and intentionally capture the data and information needed in the real-world setting.
electrocardiogram records from 380,000 patients was used to narrow down the identified DDI pairs to 8 combinations, including the ceftriaxone and lansoprazole combination. Then, patch clamp experiments were conducted and revealed significant human ethera-go-go related gene channel inhibition by the synergistic effect of this drug combination. This example highlights the use of RWD to identify a novel DDI risk and then laboratory experiments to elucidate the mechanisms of the DDI. Another recent example during the coronavirus disease 2019 (COVID-19) pandemic exemplifies how in vitro experiments followed by confirmation from RWD analysis identified clinically relevant transporter-mediated DDIs for 25 small molecule drugs evaluated in clinical trials for COVID-19. 21 Authors analyzed EHR data from the University of California, San Francisco (UCSF) Research Data Browser and Cerner Real World COVID-19 Database to confirm that the in vitro transporters medicated DDI risks were consistent with the RWD.
for discovering QT-DDIs using a database at New via data mining and laboratory experiments. (2016) 20 combination of adverse event York-Presbyterian/ Combination therapy with ceftriaxone and reports, EHRs, and laboratory Columbia University lansoprazole was associated with increased risk experiments Irving Medical of acquired long QT syndrome Center Yee et al. Identify transporter-mediated DDIs EHR data from Majority of the drugs were predicted to cause at (2021) 21 of 25 small molecule drugs being UCSF Research least one clinical DDI. COVID-19 patients should evaluated for COVID-19 treatment Data Browser and be carefully monitored for adverse reactions likely via in-vitro experiments and real- Cerner Real World to result from these DDIs world data COVID-19 Database Inform dose Sane et al.
Characterize the time course Optum EHR Showed evidence of glomerular hyperfiltration in (2022) 30 of glomerular hyperfiltration in pediatric SCD patients and the rate of decline in pediatric and adult patients with adult SCD patients. Results could help clinicians SCD in anticipating the need for dose adjustment due to renal impairment in SCD patients Provide Chanu et al. Use RWD/RWE to supplement IPDN database Confirmed the model simulated treatment insights for (2020) 32 modeling work based on RCTs to outcomes in pediatric patients receiving C. E. R. A. pediatric plan optimize C. E. R. A. development i. v. and s. c. and provided a strong rationale for development in the confirmatory trial of the applying the C. E. R. A. S. c. dosing regimen only and study pediatric plan in pediatric patients rather than both i.
Materials and Methods Data source: Data were compiled by the Marseille PCC, which investigates all exposure reported to the southeastern France poison center. Our department covers the entire French Mediterranean coast, Corsica, and tropical islands (Reunion Island and Mayotte). File selection: All files reporting botanical exposition and self-poisonings, from 1 January 2002 to 31 December 31 2021. Cases involving animals or unintentional botanical intoxication in children and duplicate cases with no real plant exposure (information or prevention calls) were not included in this study. Data collected: For each case, the following parameters were described: age, sex, circumstance/location of exposure and medical background, plants involved, co-ingestion, symptoms, treatment, and evolution. The severity of intoxication was calculated using the Poisoning Severity Score (PSS)  [26].
A blinded review of each file was realized twice by two medical toxicologists. In case of divergence between the two reviews, a third review was performed by a third clinical toxicologist. Statistics: Analysis was performed using IBM SPSS version 20 software. Categorical data were described as percentages and quantitative variables as averages with standard deviation. The Chi-square test or Fisher exact test were used to compare distribution of qualitative variables, and the Student t-test was used to compare continuous variables. A significance threshold of 0. 05 was adopted for all the statistical analysis.
Study Design Ambispective observational study of correlation between genetic variants and clinical parameters in patients up to 18 months-old, diagnosed with oncological disease and having received or receiving chemotherapy at Hospital Universitario y Politécnico La Fe (Valencia, Spain). Retrospective recruitment comprises biobanked samples from January 2007 to September 2016, and prospective recruitment from October 2016 to August 2019. Clinical and analytical data (neoplasm, chemotherapy and other drugs, hematological support, clinical and analytical toxicity, epidemiological and diagnostic data, survival) from all the genotyped patients were available in the electronic or paper medical records (Figure  1 ). Data were abstracted from patients' medical records by two independent pediatric oncologists.
Clinical and Analytical Toxicity: Survival Data Antineoplastic drug-derived toxicities were grouped as qualitative variables (grades 0-5). The grade of toxicity assigned to each group was the maximum degree observed for the patient in all the specific toxicity categories within the group following the CTCAE 4. 0 (Common Terminology Criteria for Adverse Events 4. 0) classification scale. We evaluated the hematologic, infectious, digestive, renal, neurologic, respiratory, allergic, dermatologic, otologic, and ophthalmologic organic toxicities during the period of time corresponding to induction in solid tumors and previous to maintenance therapy or hematopoietic progenitor transplantation in the leukemia patients. For survival data, overall survival and event-free survival were recorded from diagnosis to the last registered visit or exitus, ranging from 43 to 6.
Genetic Variants 2. 3. 1. SNPs Selection The pharmacogenetics panel configuration (Supplementary Materials, Table  S2 ) was based on PharmGKB database information, databases of scientific articles on this topic published from 1990 to the present (PubMed, MEDLINE, Cochrane Central Register), and journals in the pediatrics, oncology, and hematology fields. DNA extraction: The "Nucleospin Blood" (Macherey Nagel, Düren, Germany) mini kit was used following the manufacturer's instructions. DNA was eluted in TE (Tris-EDTA) and preserved upon its genotyping. DNA quality control: the final concentration and purity of DNA were evaluated by spectrophotometry with a NanoDrop 2000 (NanoDrop Technologies Inc. , Wilmington, DE, USA).
Statistics The statistical analyses were performed in collaboration with the Biostatistics and Data Science Unit at the Instituto de Investigación Sanitaria La Fe (Valencia, Spain). The correlation of polymorphisms with severe toxicity or overall survival (OS) and event-free survival (EFS) was calculated with logistic regression and Cox regression, respectively, both penalized with Elastic Net. The association of clinical outcomes with pharmacogene variants was evaluated with R software (version 4. 1. 0) by fitting logistic or Cox regression models penalized with elastic net (alpha: 0. 5; lambda: min; 500 iterations). Elastic net is a type of regularized regression that combines the strengths of Lasso and Ridge regression. It uses variable selection like Lasso and deals with multicollinearity like Ridge.
It assigns coefficients only to variables deemed important for explaining the response variable, reducing the risk of overfitting in datasets with more variables than observations, which is the case in this study (and "omic" studies in general) that have a much larger number of variables included than the number of samples (patients) analyzed. Using elastic net as a predictive model-building method does not give confidence intervals or p-values, as these are typical of hypothesiscontrast studies, which are not suitable as they are not able to include all the covariates and confounding factors that we are able to include with the current method  [16, 17]. The models included as covariates for each patient all the SNPs included in the panel, age at diagnosis, type of tumor, drugs received, and the clinical data described in Section 2.
Conclusions The results of the present work encourage further studies in order to confirm the findings. Increasing scientific contributions support the use of pharmacogenetics as a tool to guide the optimization of treatments, regarding both toxicity and the pharmacological response of patients. Pediatric oncologists are becoming aware that these strategies could make a difference in patients' clinical management, especially in vulnerable populations. Confirming the findings of this pilot study could bring some of the proposed SNPs to a level of clinical evidence high enough to include them in the guidelines for chemotherapeutic drug use in infants, a population with special vulnerability. The present work is a pilot study, and it has several limitations that we need to keep in mind to prevent unreasonable expectations: It has a small sample size regarding statistics (although a large one if we take into account that the patients included are very rare); it mixes together patients with different tumor types (although the tumor type is included as a covariate to decipher its weight); and the clinical data were retrieved retrospectively from medical records, which is never as accurate as having a prospective design (we tried to guarantee the maximum accuracy with a well-designed clinical data sheet and two different pediatric oncologists collecting the data).
Optical microscopy Before exposure to the release medium: Pictures of ensembles of microparticles were taken using a Nikon SMZ-U microscope (Tokyo, Japan), equipped with an AxioCam ICc1 camera (Carl Zeiss, Jena, Germany). The Axiovision Zeiss Software was used for data treatment. After exposure to the release media: Microparticles were treated using the method described for the in vitro drug release studies from single microparticles (see Section 2. 3. ). At predetermined time points, pictures were taken using an Axiovision Zeiss Scope-A1 microscope, equipped with an AxioCam ICc1 camera. The diameters of the microparticles were determined with the Axiovision Zeiss Software.
Microneedle design for biomedical applications, such as transdermal drug delivery, vaccination and transdermal biosensing, has lately become a rapidly growing research field. In this sense, finite element analysis has been extendedly used by microneedle designers to determine the most suitable structural parameters for their prototypes, and also to predict their mechanical response and efficiency during the insertion process. Although many proposals include computer-aided tools to build geometrical models for mechanical analysis, there is a lack of software utilities intended to automate the design process encompassing geometrical modeling, simulation setup and postprocessing of results. This work proposes a novel MATLAB-based design tool for microneedle arrays that permits personalized selection of the basic characteristics of a mechanical model.
Finite element modeling (FEM) software permits developing a wide range of static and dynamic simulations, where the critical loads before failure and insertion process parameters can be obtained. These analyses require suitable MN and/or skin models, which could be assisted and automated via CAD tools. To the best of the authors' knowledge, many proposals make use of different CAD software to facilitate geometrical description of their designs  [10, 20, 21, 23, 24] , but none comprises automation of the design process, from selection of modeling and geometrical parameters to analysis of the corresponding FEM simulation results with minimum user interaction. In this work, a novel design tool for MN design based on mechanical characterization by finite element analysis is proposed. The MATLAB-based tool presents a graphical user interface (GUI) that permits personalized selection of the basic characteristics of the mechanical model of a single microneedle or an MN array, such as solid or hollow structure, conical or pyramidal geometry, height, base and tip areas, bore radius and offset, number of elements forming a one-or two-dimensional array, interspacing, element type or meshing size.
Once the MN model has been configured, the tool exports all the selected options to an ANSYS batch file including not only this dataset but also all the necessary instructions to run automatically both static and linear buckling analyses for axial load reproducing skin resistance to puncture. This batch file can be executed directly, both in interactive and batch mode, under a Mechanical ANSYS APDL environment, and the subsequent simulation results are retrieved by the tool for on-screen display. This additional feature allows the designer to compare the simulation results with analytical and/or experimental data, to apply optimization algorithms or to derive mathematical expressions relating a mechanical response with a certain parameter variation, to name a few examples of potential applications.
ANSYS Mechanical APDL ANSYS Parametric Design Language (APDL) provides the user with a wide range of flexible and powerful features. It permits defining as a parameter any part of the model, such as geometry, loads or material properties. This possibility facilitates design variations intended for optimization, root cause analysis or detailed exploration of alternatives as the process will simply consist of changing one or several parameters and rerunning the simulation. Further, ANSYS APDL allows creation and execution of macros, handling arrays of parameters and includes basic logic instructions (do, repeat, if, else, then. ). In addition, the ANSYS Mechanical APDL software family provides other interesting features apart from APDL coding, such as optimization (requires a parameterized input file), probabilistic (random variation of input parameter), submodeling (two-step process with coarse and fine meshing to obtain the boundary conditions and the final results, respectively), substructuring (a stiffness matrix replaces a section of the model created separately as a substructure), user-programmable features (elements, loading, materials and commands)  [53].
In order to communicate with ANSYS, the user can select among three options: the graphical user interface (Mechanical APDL user interface M-APDL-UI), the command prompt window located within the M-APDL-UI and input and batch files. The M-APDL-UI is undoubtedly the easiest and most friendly way to interact with the software but presents important limitations. Following the necessary steps to define a model using the GUI is slower than making use of the commands directly. Additionally, the GUI offers fewer of capabilities than other options do. For those users that have better knowledge and longer experience using ANSYS, the command prompt permits introducing commands directly while still benefiting from the GUI interactivity instead of using the menus, in a faster and more efficient way. Finally, the input and batch text files follow the structure of one command per line, and the main difference between them is that the second must start with a first line of code that includes a /BATCH command.
MATLAB App Designer Released for the first time with the R2016a MATLAB version, App Designer is an interactive environment intended for rapid and intuitive development of app layouts and programming  [55]. An app is a program that includes a graphical user interface so that the person utilizing it could provide input data and obtain output data in return in a more visual and user-friendly way than a text-based environment. App Designer offers an extensive library of interactive UI components, as well as a grid layout manager to distribute and arrange the different components on screen. It also provides useful features as callback functions, which are activated by a certain event or interaction with the user, or the possibility of combining multiple apps in a single environment and interchange data among them.