dataesr/scientific-paragraphs-categorization huggingface dataset:
Droplet digital PCR Droplet Digital PCR was used to determine copy number variation in genome-edited mice and to quantify deletions in edited mESCs. Experiments were performed as duplex reactions, where the sequence employed as a donor was amplified using a fluorescein amidite (FAM)-labelled assay selected from a Universal Probe Library (UPL) set (Human, sourced from Roche, Basel, SZ). Suitable probes and primers were identified using the ProbeFinder software at the Roche assay design centre (accessible from www. universalprobelibrary. com, Supplementary Table  S2 ). In cases where a UPL set was not available, custom assays were ordered from LGC Biosearch Technologies (Novato, USA). UPL or custom assays were used in parallel with a VIC-labelled reference gene assay (Dot1l, sourced from ThermoFisher) set at two copies (CNV2) on the Bio-Rad QX200 ddPCR System (Bio-Rad) as per Codner et al.
The plate containing the droplet amplicons was subsequently loaded into the QX200 Droplet Reader (Bio-Rad). Standard reagents and consumables supplied by Bio-Rad were used, including cartridges and gaskets, droplet generation oil and droplet reader oil. Copy numbers were assessed using the QuantaSoft Analysis Pro™ software using at least 10 000 accepted droplets per sample. The copy numbers (mice) or ratio compared to internal control (mESCs) were calculated by applying Poisson statistics to the fraction of end-point positive reactions, and the 95% confidence interval of this measurement is shown. When visualizing ddPCR quantification in pools of cells the mean and 95% confidence intervals are shown.
Linear regression modelling of ddPCR data Multiple linear regression was performed in R using the 'lm' and 'predict' functions. The model was fit using the formula 'y ∼ log(x) + a', where 'y' (frequency of deletion) was the response variable, 'x' (proximity to sgRNAs) was explanatory variable one and 'a' (sgRNA cutting efficiency) explanatory variable two. Data used to fit the model were empirically determined by ddPCR in eight different targeted cell populations and at two different loci. Frequency of deletion at a particular region was calculated by dividing the relative concentration determined by ddPCR in the transfected sample by the corresponding non-targeting control. Proximity to sgRNA binding sites was determined from the midpoints of the ddPCR amplicon and sgRNA. The sgRNA cutting efficiency in each sample was the highest frequency of deletion directly at a sgRNA target site as determined by ddPCR.
Microhomology scoring and quantification Deletions were considered LDs if they spanned >200 bp from sgRNA target sites and ablated at least one S-R primer binding site. All of the larger deletions that could be resolved by PCR and Sanger sequencing were quantified (Supplementary Table  S5 ). Deletions were considered to be of expected size if indels reached ≤25 bp beyond the protospacer adjacent motif (PAM) site of the nearest sgRNA binding site. LDs previously described in the literature  (8, (19) (20) (21) (22) (23) (24)  were considered if they spanned >80 bp away from predicted sgRNA cut sites. Microhomology scoring was performed using a custom R script and the package Biostrings. 10 bp in the 5 and 3 direction at up and downstream breakpoints was used to search for microhomologies. Identical bases were given a score of 1 and mismatches were given a score of 0.
The highest scoring homology at each breakpoint site (either upstream or downstream) was considered. Bases were considered homologous if they were identical and directly abutted the deletion, as previously described  (39). To simulate the background distribution of microhomologies in the genome, random genomic locations were selected using a custom R script and the package BSgenome. The length of the simulated LDs was set to the average length of LDs identified. Chance expectation of homology at any two locations for a k-mer of a given length was calculated as previously described (40) using the equation P(x) = (x + 1)(1/4) x (3/4) 2. Alternative microhomologies were counted within deleted sequences (excluding sequences between paired sgRNAs) using R and Biostrings. Statistical analysis was performed in graph pad prism and R using 2 test, Kruskal-Wallis with Dunn's multiple comparison test, or two-tailed Mann-Whitney test as indicated.
Libraries were normalised to 4nM and pooled before sequencing using Illumina MiSeq v2 300 cycle paired end kit. Raw fastq files have been deposited in NCBI's Gene Expression Omnibus  (41)  and are accessible through GEO Series accession number GSE130621 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE130621). Fastq files were trimmed using trim galore. For visualization, trimmed fastq files were flashed  (42)  and mapped using bwa mem v 0. 7. 12. Sam files were converted to bam using samtools  (43) , converted to bigwig using deeptools bamCoverage (optionbs 1) (44), and visualised using UCSC genome browser. For analysis of individual alleles trimmed fastq files were analysed using CRISPResso v1. 0. 8  (45). A custom R script was used to quantify microhomologies in simple deletions (spanning a contiguous region and free of insertions or mutations within 10 bp of the deletion) using the same microhomology scoring criteria as at LDs.
ACKNOWLEDGEMENTS We thank  Yavor Bozhilov  and  Damien Downes  for technical advice and critical reading of the manuscript. We thank  Mohsin Badat ,  Joost Van Haasteren , and  Ron Schwessinger  for technical advice. We thank  Jim Hughes ,  Emanuele Azzoni ,  Christina Rode ,  Ruth Williams  and  Tatjana Sauka-Spengler  for helpful discussions and advice.
Acknowledgments X. M. would like to thank  Claudia García-García  for fruitful discussions. This work has been partially supported by  CONICET  and  ANPCyT  under projects PICT  2016-0164  (E. A. , N. M. , A. S. ), PICT 2017-2751 (E. A. , N. M. , A. S. ) and PICT 2017-2765 (E. A. ). This work is supported by the  European Union  through the ITN  ELUSIVES H2020-MSCA-ITN-2015//674896  and the  RISE INVISIBLE-SPLUS   H2020-MSCA-RISE-2015//690575 , by the  CICYT  through the project  FPA2016-78645-P , and by the  Spanish MINECOs "Centro de Excelencia Severo Ochoa" Programme  under grant  SEV-2016-0597.
Objective To determine real-world trends in antidiabetic drug use, and persistence and adherence, in Japanese patients with type 2 diabetes mellitus (T2DM). Design Retrospective evaluation of administrative claims data (2011-2015) using the Japan Medical Data Center (JMDC) and Medical Data Vision (MDV) databases. setting Analysis of two administrative claims databases for Japanese patients with T2DM. Participants Adults (aged ≥18 years) with an International Classification of Diseases, 10th Revision code of T2DM and at least one antidiabetic drug prescription. Main outcome measures Treatment patterns in untreated (UT) or previously treated (PT) patients receiving antidiabetic therapy; persistence with treatment at 12 months; adherence to treatment at 12 months. results 40 908 and 90 421 patients were included from the JMDC and MDV databases, respectively.
The most frequently prescribed therapy at the index (first prescription) date was dipeptidyl peptidase-4 inhibitor (DPP-4i) in UT patients (JMDC: 44. 0%, MDV: 54. 8%) and combination therapy in PT patients (74. 6%, 81. 1%). Most common combinations were DPP-4i plus: biguanide (BG; 11. 4%, 10. 9%), sulfonylurea (SU; 8. 4%, 11. 0%) or BG+SU (7. 8%, 9. 1%). In UT or PT patients from either database whose index prescription was for any antidiabetic drug class(es) other than DPP-4i, the most frequent add-on or switch was to DPP-4i. 12-month persistence with index monotherapy was highest with DPP-4i and BG. Adherence was high (≥80%) for all monotherapy schedules, except insulin and glucagon-like peptide-1 agonist, and for the five most frequent two-drug and three-drug combinations. Persistence was greater in elderly UT patients and in those receiving ≤5 medications, but comparatively worse in UT patients with ≥3 index antidiabetic drug classes.
IntrODuCtIOn The prevalence of diabetes mellitus continues to increase globally. In 2015, approximately 415 million people worldwide had diabetes, and this figure is projected to reach almost 650 million by 2040. 1  As about 20% of men and 10% of women in Japan are considered to have or are highly likely to have diabetes, the public health implications are enormous. 2 isease characteristics in Asian individuals with type 2 diabetes mellitus (T2DM) differ from those in Caucasian patients; Japanese strengths and limitations of this study ► This retrospective evaluation of administrative claims data (2011-2015) using the Japan Medical Data Center (JMDC) and Medical Data Vision (MDV) databases was conducted to determine real-world trends in antidiabetic drug use, and persistence and adherence, in Japanese patients with type 2 diabetes mellitus (T2DM); 40 908 and 90 421 patients were included from the JMDC and MDV databases, respectively.
► The main strengths of the study are that it provides robust real-world evidence from two large administrative claims databases for patterns of antidiabetic drug use in Japanese patients with T2DM, highlighting widespread use of dipeptidyl peptidase-4 inhibitor (DPP-4i) schedules (as monotherapy, add-on therapy, switch therapy or in combination regimens) and marked persistence and adherence with DPP-4i therapy. ► The study was limited to some extent by the strict inclusion criteria which restricted the number of patients eligible for analysis, and by the use of prescription events rather than patient-derived data to estimate outcomes. ► Database-specific limitations were the relative scarcity of data for patients aged ≥65 years (JMDC), the absence of information as to whether patients received care in other medical facilities (MDV), and the inability to examine reasons for treatment discontinuation and potential health benefits resulting from increased persistence (JMDC and MDV).
1 20 21  Despite widespread recognition of the deleterious longterm consequences of poorly managed T2DM, and the Open access proven efficacy of incretin-based therapies in Asian populations with diabetes, surprisingly little is known about actual antidiabetic drug utilisation trends, and persistence and adherence patterns with antidiabetic drug therapy, in patients with T2DM in Japan. Under Japan's compulsory insurance system, all residents are legally obligated to be covered by a form of public health insurance, and claims-related data are captured and stored in propriety databases. In the current study, data from two large administrative claims databases were used to determine real-world trends in antidiabetic drug use, and treatment persistence and adherence rates, in patients with T2DM in Japan.
Overview This was a real-world, retrospective evaluation of data from two administrative claims databases in Japan: the Japan Medical Data Center (JMDC) database (Japan Medical Data Center; Tokyo, Japan) and the Medical Data Vision (MDV) database (Medical Data Vision; Tokyo, Japan). The JMDC database contains monthly claims submitted to health insurance societies from medical institutions since January 2005 and, as at July 2017, covered approximately 4 million beneficiaries (employees and their dependants). MDV is a nationwide hospital-based claims database covering nearly 19 million cumulative patients since April 2008 who, as at July 2017, had been treated as inpatients or outpatients at the approximately 300 hospitals in Japan (20% of total number of hospitals) that participate in the diagnosis procedure combination/ per-diem payment system.
The minimum 12-month postindex observational period allowed time to evaluate treatment-related outcomes of interest. Patients were excluded for the following reasons: age <18 years at the index date; <12 months of continuous enrolment in the database before or after the index date; index prescription received in the 12 months before the index date; no T2DM diagnosis (ICD code E11 or E14) in the preindex period (figure  1 ). The patient population was divided into two subgroups: (1) untreated (UT) patients, that is, patients without a prescription for any antidiabetic drug class of interest during the preindex period; and (2) previously treated (PT) patients, that is, patients with a prescription for at least one nonindex antidiabetic drug class during the preindex period.
Antidiabetic drug classes of interest Target antidiabetic drug classes of interest were DPP-4i, BG, SU, α-GI, TZD, glinides, SGLT2i, insulin and GLP-1 receptor agonists and, in PT patients, were the five most common combinations of these same drug classes. Data for insulin and GLP-1 receptor agonists were excluded from persistence and adherence analyses mainly because of inconsistent database information regarding the duration of therapy for these injectable drug classes.
As the JMDC and MDV databases each contain a field corresponding to the number of days' supply of a medication, these data were used to calculate the number of prescription days. Patients were considered adherent if a PDC of ≥0. 8 (also expressed as an adherence rate of ≥80%) was achieved. The PDC was calculated from the index date to the first discontinuation of index treatment. Adherence/persistence was calculated according to the number of antidiabetic drug prescription days, without differentiating between inpatient/outpatient prescribing. No information was available about possible pill dumping or stockpiling. Open access statistical analyses Analyses were performed using SAS V. 9. 3 (SAS) and were conducted on all patients who met the inclusion criteria and were stratified into the two prespecified patient subgroups (UT and PT) on the index date.
Patient disposition Between January 2011 and December 2015, 94 529 patients in the JMDC database and 721 366 patients in the MDV database with at least one prescription for an antidiabetic drug class of interest were identified. Of these, 40 908 patients (43. 3%) in the JMDC database and 90 421 patients (8. 0%) in the MDV database met the inclusion criteria and were included in the analyses (figure  1 ). The ratio of UT to PT patients was approximately 1:1 in the JMDC database and 1:3 in the MDV database.
Patient characteristics Patient demographics and clinical characteristics are presented in table 1. The median duration of follow-up in UT patients was 929 days in the JMDC database and 942 days in the MDV database. Mean age was 51. 7 years and 67. 6 years, respectively. There was a higher proportion of males (72. 3% vs 60. 8%), a lower mean number of concurrent medications (2. 0 vs 3. 0) and lower incidences of comorbid hypertension (47. 8% vs 70. 1%), hyperlipidaemia (39. 8% vs 70. 0%), dementia (0. 2% vs 1. 9%) and diabetic nephropathy (3. 7% vs 18. 1%) among UT patients in the JMDC versus MDV database. Among PT patients, the median duration of follow-up was 980 days in the JMDC database and 1027 days in the MDV database. Mean age was 54. 4 years and 66. 9 years, respectively. There was a higher proportion of males (73.
5% vs 61. 2%), a lower mean number of concurrent medications (2. 3 vs 3. 3) and lower incidences of comorbid hypertension (58. 3% vs 71. 3%), hyperlipidaemia (50. 0% vs 67. 2%), dementia (0. 2% vs 2. 0%) and diabetic nephropathy (6. 1% vs 15. 7%) among PT patients in the JMDC versus MDV database.
4%; MDV: 21. 6%), SU (JMDC: 24. 5%; MDV: 22. 8%), or insulin, but only in the MDV population (JMDC: 0. 7%; MDV: 24. 6%). In PT patients whose index prescription was for any other antidiabetic drug class, the most frequent add-on was a DPP-4i to all drug classes except GLP-1 receptor agonists in the JMDC database and was a DPP-4i to all drug classes except α-GI and GLP-1 receptor agonists in the MDV database (table  4 ). In PT patients whose index treatment was a DPP-4i, the most frequent treatment switch was to insulin (JMDC: 35. 0%; MDV: 30. 1%). In PT patients whose index prescription was for any other antidiabetic drug class, the most common treatment switch in either dataset was to a DPP-4i or to insulin (table  5 ).
Persistence and adherence with index monotherapy In both patient subgroups across both databases, the probability of remaining on treatment with index monotherapy at 12 months (not including insulin and GLP-1 receptor agonists) was highest with DPP-4i schedules and lowest with glinide schedules (table  6 ). This is illustrated schematically by Kaplan-Meier survival curves showing the distribution of median time to treatment discontinuation during 12 months' observation by index antidiabetic drug class for UT and PT patients in each database. Among UT patients, persistence with all antidiabetic drug classes was considerably lower in the JMDC database especially with glinide schedules (figure  3A ), than in the MDV database (figure  3B ). Among PT patients, persistence with all antidiabetic drug classes tended to be slightly lower in the JMDC database for all antidiabetic drug classes except TZD and especially for glinide schedules (figure  3C ) than in the MDV database (figure  3D ).
Twelve-month persistence rates of approximately 50% or less were recorded for SU, α-GI, TZD and glinides in one or both patient subgroups from one or both datasets (table  6 ). Adherence to index antidiabetic drug classes (not including insulin and GLP-1 receptor agonists) was high in both patient subgroups across both databases, with rates ranging from 75. 0% to 98. 9%. In UT patients (figure  4A ) and in PT patients (figure  4B ), adherence rates with index antidiabetic drug classes were consistently lower in the JMDC database than in the MDV database. The lowest adherence rates were recorded with SGLT2i in UT patients (75. 0%) and PT patients (77. 0%) in the JMDC database.
Persistence and adherence with index combination therapy For the five most common antidiabetic drug combinations prescribed to PT patients on the index date (ie, a DPP-4i plus BG, SU, BG+SU, α-GI, or SU+α-GI), Open access 12-month persistence rates were highest for DPP-4i plus BG (JMDC: 53. 7%; MDV: 72. 0%) and lowest for DPP-4i plus SU+α-GI (JMDC: 30. 8%; MDV: 64. 2%) (figure  5 ). Overall, 12-month persistence rates were considerably lower in the JMDC versus MDV database (figure  5 ). For the five most common antidiabetic drug combinations prescribed to PT patients on the index date (ie, a DPP-4i plus BG, SU, BG+SU, α-GI, or SU+α-GI), adherence rates were ≥80% in both database populations although were slightly lower in the JMDC versus MDV database (figure  6 ).
Principal findings This real-world evaluation of data from two administrative claims databases in Japan reveals that the most common index antidiabetic drug class was DPP-4i in UT patients (44%-55%) and combination therapy in PT patients (~75%-80%), with the latter most frequently comprising dual therapy with a DPP-4i plus a BG or SU. Among patients with a change to their index antidiabetic drug therapy during follow-up: the most common add-on to DPP-4i index therapy was a BG or SU; the most common add-on to BG or SU index therapy was a DPP-4i; the most common switch from DPP-4i index therapy was to a BG or SU; the most common switch from index drug classes other than DPP-4i (except GLP-1 receptor agonists) was to a DPP-4i. Overall patterns for add-on or switch therapy were similar between the JMDC and MDV datasets and between UT and PT patients.
Across all four patient subgroups, 12-month persistence rates were highest with index DPP-4i monotherapy compared with all other index antidiabetic drug classes, although did not exceed 78. 8% (with DPP-4i in PT patients in the MDV database) and were around 50% or less with several index antidiabetic drug classes especially in the JMDC database. Mean adherence to antidiabetic monotherapy was high overall, and the proportion of patients with high adherence (≥80%) was higher with index DPP-4i than with all other antidiabetic drug classes. Among drug combinations, 12-month persistence rates were higher for DPP-4i plus BG than for other combinations, although did not exceed 72. 0%. Adherence rates were ≥80% for commonly prescribed antidiabetic drug combinations. We also analysed persistence (≥12 months, <12 months) and drug adherence (<80%, ≥80%) in UT patients according to other patient-related and treatment-related factors.
Persistence tended to increase with age (online supplementary table  1 ). In the JMDC database, the Open access adjusted OR for non-persistence was 3. 31 (p<0. 05) in the 65-74 years age group compared with the reference group (18-34 years). In addition, persistence with multiple medications tended to be good in patients receiving ≤5 medications, but poorer in patients receiving ≥6 medications. In the MDV database, 29. 1% of patients with 4-5 medications were non-persistent, whereas 47. 6% of patients with >8 medications were non-persistent. Persistence was good in patients with comorbid hypertension (JMDC: 66. 0%; MDV: 71. 4%) or hyperlipidaemia (JMDC: 62. 3%; MDV: 73. 6%). However, persistence was poor in patients treated with multiple antidiabetic drug classes: in both the JMDC and MDV databases, approximately 60%-70% of patients receiving ≥3 index antidiabetic drug classes were non-persistent.
Similar findings were evident for adherence (online supplementary table  2 ). In the MDV database, only 2. 0% of patients receiving antidiabetic monotherapy were non-adherent, whereas 6. 6%-9. 5% of those with ≥3 antidiabetic drugs were non-adherent. All these findings are interesting and suggest that higher rates of persistence and adherence observed in elderly patients treated with multiple medications may reflect greater insight by this group into their disease. Conversely, the relatively low rates of persistence and adherence in patients treated with more index antidiabetic drug classes may have resulted from patient or caregiver difficulties regarding drug management. Therefore, FDC therapy, with its potential to enhance persistence and adherence, may be especially appropriate for patients treated with several index oral antidiabetic drug classes.
strengths and limitations of the study The main strengths of the present study are that it provides robust real-world evidence from two large administrative claims databases for patterns of antidiabetic drug use in patients with T2DM in Japan, clearly highlighting the widespread use of DPP-4i schedules (as monotherapy, add-on therapy, switch therapy or in   Open access combination regimens), and shows marked persistence and adherence with DPP-4i therapy. The study was limited to some extent by the strict inclusion criteria, which restricted the proportion of patients from each database eligible for analysis. The analyses did not factor in HbA1c levels at the start of treatment, or the level of HbA1c control achieved during treatment, which may have influenced treatment decisions. Another limitation was the use of prescription events, rather than patient-derived data (eg, patient diaries), to estimate outcomes.
A limitation specific to the JMDC database was the relative scarcity of data for patients aged ≥65 years. A limitation specific to the MDV database was the absence of information about whether patients received care in other medical facilities. For example, receipt of a prescription at another medical facility could result in a missing medication history and misclassification of the patient in our analysis. The inability to examine reasons for treatment discontinuation or to analyse any potential health benefits (eg, reduced symptom severity or improved health-related quality of life) resulting from increased persistence, as such data are not collected in administrative claims databases, were limitations that applied to both databases. Lastly, the study may not have accurately captured the uptake of SGLT2i use given the timing of their introduction in Japan.
Figure5Twelve-month persistence rates with the five most frequent index antidiabetic drug combinations in PT patients in the JMDC and MDV databases. α-GI, α-glucosidase inhibitor; BG, biguanide; DPP-4i, dipeptidyl peptidase-4 inhibitor; JMDC, Japan Medical Data Center; MDV, Medical Data Vision; PT, previously treated; SU, sulfonylurea.