dataesr/scientific-paragraphs-categorization huggingface dataset:
Collaborators contributing NHD were the DMD Italian Group (2008-2013), the iMDEX study (2012-2018)  [40]  , and the PRO-DMD-01 prospective natural history study (2012-2016)  [41]. Data from the iMDEX study were provided to cTAP by University College London on behalf of the Association Française contre les Myopathies (AFM). Data from the PRO-DMD-01 study were provided by CureDuchenne.
Ethics approvals RWD/NHD sources were approved by ethics committees from each institution (the University Hospitals Leuven [Leuven], Catholic University, Rome [DMD Italian Group], each participating center of the PRO-DMD-01 study, and the institution review board at the CCHMC [IRB #2010-1881]). For the iMDEX study, ethics review boards at the participating institutions approved the study protocol, consent and assent documents. For use of the North Star UK data, this project followed Caldicott Guardian regulations and information was entered in the database after written informed consent was obtained from patients' parents. Only anonymous, de-identified data were analyzed. All clinical investigations were conducted according to the principles expressed in the Declaration of Helsinki, following Caldicott Guardian approval.
Potential prognostic factors The availability of prognostic factors by data source is summarized in Table  A. 4. Age, height, weight, type of steroid (deflazacort or prednisone), and baseline NSAA total score were available in all data sources except the DMD Italian Group and are known to be prognostic of changes in ambulatory function  [20]. The availability of additional known prognostic factors varied across data sources. In the tadalafil, ACT DMD and DEMAND III trial placebo arms, and in Leuven, PRO-DMD-01 and iMDEX, timed rise from floor, 4-stair climb (4SC) and 10-meter walk/run (10MWR), and the 6MWD were all available. Primary analyses were based on pooling data across these six sources, as they had the largest number of prognostic factors in common and available for adjustment. In the North Star UK database, rise from supine and 10MWR were available, while in CCHMC, 4SC was available along with timed sit to stand and 30 ft walk/run.
In the DMD Italian Group, 10MWR and 6MWD were available, but height, and steroid type were not available. Given these differences, three sets of sensitivity analyses were done, separately adding data from: 1) North Star UK, 2) CCHMC, and 3) DMD Italian Group, to the six data sources used in the primary analyses. In each of these sensitivity analyses, the set of prognostic factors used for adjustment was based on what was commonly available across all the included sources ( Table  A. 4  ). Dystrophin genotypes have been associated with differences in ages at loss of ambulation, long-term changes in function, and other clinical milestones in DMD  [27 , 31 , 42]. The present study did not include genotypic prognostic factors as not all patients and data sources had genotype  information available.
Sample selection Included patients were required to meet each of the following criteria during an interval of follow-up of approximately 48 weeks in length: (1) age ≥ 5 years and < 18 years at the first visit in the interval (referred to as the baseline visit); (2) receiving either prednisone or deflazacort at the baseline visit, (3) baseline NSAA total score > 12; (4) NSAA total score available at the endpoint visit of the interval; and (5) prognostic factors listed above were nonmissing at the baseline visit (if available at all in the data source) ( Fig. 1  ). Criterion (2) above could not be applied exactly as specified above in the DMD Italian Group data due to lack of availability of data on whether prednisone or deflazacort was the steroid being used. However, data on use of steroids (without mention of steroid type) was available and used to restrict the sample to patients known to be daily or intermittent steroid users.
Sensitivity analyses To assess whether consistency between trial placebo arms and RWD/NHD sources was sensitive to adjustment of different sets of prognostic factors, data from North Star UK, CCHMC, and DMD Italian Group, which included different sets of baseline prognostic factors, were incorporated in three separate sensitivity analyses. Each sensitivity analysis added one RWD/NHD data source to the set of data sources used in the primary analysis: the first added North Star UK, the  second added CCHMC, and the third added DMD Italian Group. Adjustment factors included in each of the three sensitivity analyses were limited to those available in all the included data sources.
Consistency across time periods, geographies and clinics Our studied data sources differed in terms of time periods covered, geographic representation and specific clinics, which in turn might be associated with different care patterns, including steroid treatment, and outcome assessment practices. We observed consistency in mean NSAA outcomes despite these differences in the primary analyses. Investigation of time period effects in the RWD/NHD revealed no meaningful trends over time in NSAA outcomes after accounting for baseline status. In addition, our findings were reassuringly similar across sensitivity analyses incorporating additional data sources. Analyses incorporating the North Star UK, CCHMC, and DMD Italian Group databases, in particular, added patients from two large RWD sources from different countries, with notable differences in care setting (a single center at CCHMC and multiple centers throughout the UK in North Star UK and throughout Italy in the Italian Group), steroid prescribing (predominantly prednisone in North Star UK, compared to predominantly deflazacort in CCHMC), and different functional measures available for baseline adjustment.
Average NSAA outcomes were highly consistent, after adjustment for baseline prognostic factors, across the studied care centers and time periods primarily representing the years 2006-2016. Consequently, external control groups in ambulatory DMD may not be biased by differences in geography or time period within these ranges. However, the longevity of these data sources for use as external controls into the future will need to be continually evaluated on a case-by-case basis. External controls should be drawn from time periods and geographies that are comparable to those of the treated patients, and ideally from parallel groups, to ensure that differences in standards of care do not confound outcomes.
Our findings for NSAA, in combination with earlier results for 6MWD and timed 4SC  [20 , 44]  , indicate that combinations of baseline functional measures are strongly prognostic, and should be used for matching or adjustment in comparisons to external controls in DMD. While we did not attempt to identify an optimal set of baseline factors to adjust for, we observed that adjustment for steroid type, height, weight, BMI, and multiple functional characteristics -as in our primary analysis, which adjusted for 6MWD, NSAA, and velocities for rise from supine, 4SC, and 10MWR -provided the greatest reduction in the already small differences in mean NSAA across data sources. In the sensitivity analyses incorporating DMD Italian Group, in which, unlike the primary and other sensitivity analyses, adjustment was not possible for steroid type, weight, and BMI, small differences remained between placebo arms and RWD/NHD sources, indicating the incremental importance of adjusting for these factors.
Generalizability to other RWD/NHD sources The data sources included in this study shared important similarities that likely contributed to the consistency in mean NSAA outcomes between RWD/NHD and placebo arms. Firstly, the RWD/NHD databases represent care centers with extensive research and DMD clinical trial experience, with NSAA assessment typically conducted by physicians, physical therapists, or clinical evaluators who are trained and experienced in administering the NSAA and other functional assessments in clinical trial settings. Our findings may or may not generalize to care centers that have less experience with NSAA assessments. In addition, the time periods represented by the RWD/NHD sources studied here precede the approvals of multiple targeted therapies for DMD. As more therapies are approved, differences in treatment use could introduce further variation in outcomes across care centers and data sources.
Collaboration Our goal of understanding the consistency of NSAA between RWD/NHD and placebo arm settings was accomplished by collaboratively analyzing a broad collection of data sources. The sharing of clinical data across multiple institutions, registries, and geographies was a challenge, but one worth overcoming for this important research goal. Collaborating through cTAP simplified and accelerated this process, and highlights the importance of data collection, data sharing, and collaboration for DMD drug development. Careful, evidence-based use of external controls can potentially reduce the number of patients that need to receive placebo while advancing the development of effective therapies in DMD. We conclude that the high degree of consistency in NSAA outcomes observed in the present study, the demonstrated performance of adjustment for known prognostic factors, and previously observed consistency for 6MWD outcomes, together provide a strong foundation for the incorporation of external controls into the evaluation of drug effects on these outcomes in DMD.
Acknowledgements The authors are grateful to patients for participating in the clinical assessments and agreeing to make their data available for research. The authors also thank  Eli Lilly  for contributing placebo arm data from the tadalafil DMD trial. Gloria DeWalt, PhD , an employee of  Analysis Group, Inc. , assisted with the development of this manuscript. The authors would like to acknowledge investigators and staff from each of the RWD/NHD sources included in this study. The iMDEX study is funded by the  Association Française contre les Myopathies (AFM) , while the UK North Star is funded by  Muscular Dystrophy UK. Francesco Muntoni  is partially supported by the  National Institute for Health Research (NIHR) Biomedical Research centre at Great Ormond Street Hospital for Children NHS Foundation Trust and University College London.
1 ( -1. 9, -0. 3)  * 4SC, 4-stair climb; 6MWD, six-minute walk distance; 10MWR, 10-meter walk run; BMI, body mass index; CCHMC, Cincinnati Children's Hospital Medical Center; CI, confidence interval; DMD, Duchenne muscular dystrophy; NSAA, North Star Ambulatory Assessment. Notes: Table shows difference in mean NSAA (95% CI) between RWD/NHD and trial placebo arms across different analyses. Primary analysis was based on data from the tadalafil DMD trial placebo arm, ACT DMD placebo arm, DEMAND III placebo arm, PRO-DMD-01 study, Leuven database and the iMDEX study. Each sensitivity analysis added the one mentioned data source to the core set of data sources used in the primary analysis. Primary analysis adjusted for age, NSAA total, deflazacort use, height, weight, BMI, 6MWD, and 4SC, 10MWR and rise from supine velocities.
Data sources and search strategy To identify potential papers, we searched three databases: PubMed, WebOfScience, and PsycINFO until June 2022 with the following equation terms: [("Anxiety Disorders" OR "Social Anxiety" OR "Generalized Anxiety Disorder" OR "Panic disorder" OR "Social, Phobia" OR "phobic disorder" OR "Phobia, Specific" OR agoraphobia) NOT ("Obsessive-Compulsive Disorder" OR "Anxiety, Separation" OR "Neurocirculatory Asthenia" OR "Neurotic Disorders")] AND (actigrap * OR actimet * OR actograp * OR actomet * OR accelerometer). The title and abstract of each potential paper were screened by two reviewers working independently (MP and JLC). Zotero software was used for the management of records. The full text of eligible studies was then reviewed independently by the same two reviewers to assess all inclusion and exclusion criteria.
Data extraction and quality assessment We extracted all relevant data from selected papers using a data chart. The quality of each study was assessed using a modified version of the Effective Public Health Practice Project Tool (17) that we built for the purpose of this review. We used the EPHPP tool because the designs of selected studies were highly heterogeneous. Sections D, G, and H of the original scale were not relevant because our review did not include any interventional study and were therefore suppressed from the global rating. Likewise, section C was also revised because the first part of the section (Q1, "Were there important differences between groups prior to the intervention?") was not applicable. Thus, we considered only the second part of the section (Q2, "Indicate the percentage of relevant confounders that were controlled") for the rating of section C.
Results The search retrieved 201 potential papers (80 from PubMed, 85 from WebOfScience, and 36 from Psychinfo). After the removal of duplicates (46 papers), 129 articles were excluded based on title or abstract (not relevant to the topic). Among the 26 papers that were read in full to assess eligibility, 20 were excluded because they did not fulfill the criteria. Most of the excluded papers focused on anxiety symptoms only and did not consider anxiety disorder diagnoses. Three papers  (15, 18, 19)  used pooled diagnostic data of anxiety disorders and mood disorders. We contacted the authors to obtain specific data on anxiety disorders, but we did not receive an answer. Six articles were included in the mini-review (see Table  1  for a summary of principal results). A flow diagram based on PRISMA 2020 guidelines is shown in Figure  1.
Night-time activity was characterized by sleep time (percentage of time asleep between onset and end of sleep), and sleep efficiency (ratio between actual sleep time and total time in bed). These outcomes did not change after treatment and did not differ with those of a control group of female healthy administration workers. At 4 weeks, there was no significant difference in sleep patterns between patients that showed a clinical improvement (>50% HAM-A score) and those that did not. The study by Luik et al. (20)  analyzed cross-sectionally the circadian activity and sleep patterns of patients with anxiety disorders in a populational cohort (>45 years old) from the Rotterdam Study (23). 96 h of actigraphic data was collected from 1,714 people. Anxiety disorders (n = 141) were diagnosed using the Composite International Diagnostic Interview (CIDI).
The authors also found that GAD (n = 39) was associated with more fragmented rhythms (OR: 1. 75 per 1 SD, [1. 20; 2. 55], p = 0. 004) and a shorter TST (OR: 0. 66 per 1 h, [0. 45; 0. 97], p = 0. 033) than the reference group after adjusting on covariates. Koolhaas et al. (24)  studied the relationship of anxiety disorders and sedentary behavior with the data of the Rotterdam Study. A subsample of participants was monitored with an actigraph for a period of 7 days (n = 1,841). Activity level during waking hours was measured by the number of counts per minute (a count corresponding to a single movement in any direction captured by the actigraphic sensors). For a given subject, the time of sedentary behavior corresponds to the time during which the activity is <199 counts per min. Diagnoses of anxiety disorders were obtained at baseline using the CIDI.
Participants with anxiety disorders (n = 147 prevalent cases) reported significantly more sedentary time than the rest of the sample in unadjusted analyses, but after controlling for lifestyle factors (namely disability, smoking, and occupational status) the association did no longer exist. Of note, sedentary behavior at baseline was not associated with the emergence of anxiety disorders during the average 5. 7 years of follow-up time (n = 59 incident cases). Wainberg et al. (  22 ) conducted a post-hoc cross-sectional analysis of 89 000 individual actigraphic data from the UK-Biobank (a community-based prospective cohort study) to study sleep parameters in anxiety disorders. Anxiety disorders were identified through registered codes of the International Classification of Diseases-10th edition (F40, F41).
04). Helgadóttir et al. (  25 ) used actigraphic data of 165 anxious and/or depressed Swedish adults from the Regassa randomized controlled study  (26)  to investigate their level of sedentary behavior. All participants, who had a minimum score of 10 on the Patient Health Questionnaire, wore an actigraph for seven days. Diagnoses were obtained with the MINI. They measured activity level using the same proxy described above (number of counts per minute), considering <100 counts per min during twenty consecutive minutes as a sedentary activity bout, 100-1,951 counts per minute as light PA, and more than 1,951 counts per min as moderate to vigorous activity. They then calculated the total time spent in sedentary bouts, as well as the number of sedentary bouts. Twenty-two participants were diagnosed with an anxiety disorder, while 121 had depressive and anxiety disorders at the same time.
Discussion A large share of the recent literature about actigraphic measures in psychiatry is focused on mood disorders. In contrast, we decided to review systematically the objective alterations of sleep and activity patterns associated with anxiety disorders that so far have been the object of only a handful of studies. Although the results of our review show that these symptoms can be objectively detected in anxiety disorders, for the moment there is very limited evidence supporting the use of actigraphic measures to monitor their evolution or severity. Four papers studied anxiety disorders as a general category. All of them were secondary studies, based on large datasets. Luik et al. (20)  found fragmented 24 h circadian rhythm measures in anxiety disorders and specifically in GAD, which was also associated with shorter TST.
More recently, clinical FAP-targeted PET imaging has been used to image lymphoma in addition to solid tumors  [74]. Other FAP-targeting imaging agents such as (4-quinolinoyl)-glycyl-2-cyanopyrrolidine-based small molecules  [75, 76]  and FAP-2286  [77]  are also in development; however, they have not yet been as widely used as FAPI-04. Early clinical research studies have now given way to larger studies evaluating FAPtargeted imaging agents. An ongoing clinical trial (NCT04441606) is currently enrolling patients with the goal of evaluating 68 Ga-FAPI-04 PET compared to 18 FDG-PET to evaluate disease burden, with an emphasis on malignancies with variable 18 FDG uptake  [78]. Another clinical trial (NCT04499365) plans to enroll 500 patients for 68 Ga-FAPI-04 PET to provide robust clinical data regarding the use of FAP-targeted imaging to diagnose and monitor cancer  [79].
cGAS and DNA-PKcs levels increase with tumor grade To confirm the physiological relevance of our findings that indicate that cGAS expression impairs early tumorigenesis, we first performed a meta-analysis of glioblastoma tumors, using the GlioVis database  (Bowman et al, 2017). We examined the co-expression of PRKDC and MB21D1 (encoding cGAS) in glioblastoma tissue samples from the CGGA transcriptomic database. Of the 224 retrieved cases, three statistically well-represented populations were determined based on PRKDC and MB21D1 expression levels: PRKDC low / MB21D1 low , PRKDC high /MB21D1 low , and PRKDC high /MB21D1 high   A Graph represents the mean (AE SEM) fold migration of THP-1 cells through a 3 lm transwell insert when conditioned media from T98G Empty or T98G cGAS was applied to lower chamber for 6 h.
Indeed, DNA-PK inhibitors have been used in preclinical studies in glioblastoma, bringing promising results  (Lan et al, 2016 , Timme et al, 2018) , and several clinical trials are ongoing (NCT02977780 and NCT04555577). To fully exploit the benefit of DNA-PKcs inhibition, our data support that the expression of cGAS is an important parameter to consider. Conversely, DNA-PK agonists could allow the re-establishment of inflammatory responses in tumors in which the cGAS pathway is not functional, or boost anti-tumoral ▸ Figure  6. cGAS and DNA-PKcs levels increase with tumor grade. A Correlation plot between PRKDC and MD21B1 expression in glioblastoma patients. Three distinct populations can be visualized: PRKDC low /MD21B1 low ; PRKDC high / MD21B1 low and PRKDC high /MD21B1 high (total patients n = 224).
B Violin plots present chemokine gene expression (CXCL10, CCL2 and CCL5) in glioblastoma samples from (A). C Violin plots present macrophage gene expression (CD68 and ITGAM) in glioblastoma samples from (A). D Violin plots present pro-inflammatory M1 macrophage gene expression (CD86 and CD80) in glioblastoma samples from (A). E Violin plots present anti-inflammatory M2 macrophages gene expression (CD200R1 and CD209) in glioblastoma samples from (A). F Violin plots present M1/M2 gene expression ratio, calculated using the mean expression of the genes indicated in (D) and (E). G Graph presents the survival rate of glioblastoma patients from A that present PRKDC low /MB21D1 low , PRKDC high /MB21D1 low and PRKDC high /MB21D1 high expression. H Violin plots present the expression of PRKDC and MB21D1 in datasets analyzed in A, based on tumor grade (II to IV).
Our study raises the possibility that in inflammatory pathologies presenting with pathological chronic STING activation, inhibition of DNA-PKcs in combination with classical Janus kinase inhibitors, which are already used in standard patient care  (Sanchez et al, 2018) , may allow better suppression of chronic type I IFN responses. Conversely, PRKDC mutations are associated with autoinflammatory pathologies  (Mathieu et al, 2015; Esenboga et al, 2018)  in which cGAS-STING activation should be explored for the design of novel therapeutic strategies. Summary of the expression patterns of DNA-PKcs and cGAS in human glioma samples. The correlation between DNA-PKcs and cGAS protein levels was analyzed using SPSS Pearson Chi-Square test (R = 0. 406, P = 0. 00568). A P-value of < 0. 05 was set as the criterion for statistical significance.
Secondary antibody incubation was performed for 1 h at RT. Secondary antibodies used are Alexa Fluor 488 goat anti-Mouse IgG, (#A11001, Thermofischer) and Alexa Fluor 594-coupled goat anti-Rabbit IgG (#R37117, Thermofischer). Nuclei were stained with DAPI and coverslips mounted in Vectashield mounting media. Images were acquired by Apotome Z3 microscope by Zeiss with ZEN (blue edition) software with a 63X oil objective, and images were processed with Omero or Fiji.
Spheroids T98G cells lines were prepared in DMEM media, and 600 cells were distributed per well of 96 well plates (Corning ultralow attachment) prior to centrifugation for 2 min at 200 g. Cells were kept in the incubator during 72 h to allow spheroid formation prior to daily scanning using a Celigo cell imaging cytometer (Nexcelom) apparatus until day 8. Volume was estimated using Celigo software.
Analysis of zebrafish images To estimate tumor mass, Z-projections were performed using the Zen software (ZEISS). A threshold was then applied on the Fiji software for each embryo at D0, D1, and D2 to determine tumor area. Tumor area was next normalized to the day of transplantation (D0) to evaluate tumor growth of the injected cell lines. Macrophage recruitment around the tumor site was estimated by manual counting on Fiji, by moving the focus in all Z stacks. To characterize invasiveness, the elongated cells were counted manually on Fiji. Images were prepared for publication using the Fiji program by setting the same parameters in all compared images. Glioblastoma RNA seq data retrieval and correlation analysis For mRNA expression analysis, RNA seq data of CGGA-GBM data set, containing 224 glioblastoma samples was used.