dataesr/scientific-paragraphs-categorization huggingface dataset:
Association with Drug Sensitivity Ex vivo drug response in BEAT-AML samples was measured through the Area Under the doseresponse Curve (AUC) metric, wherein a low AUC corresponds to sensitivity while a high AUC corresponds to resistance. AUC values were scaled and multiplied by -1 to represent sensitivity in each treatment condition. Pearson correlation was used to measure association between cell type abundance and drug sensitivities, following recommendations from a benchmarking study by Smirnov et al  95. Associations were depicted using the R package "corrplot", and drug sensitivity volcano plots were generated using the R package "EnhancedVolcano".
Unsupervised Clustering by Drug Sensitivity Unsupervised clustering of 30 AML patient samples from Lee et al  53  was performed on the basis of their ex vivo drug sensitivity values to 159 drugs. Area under the dose response curve (AUC) values for each patient were scaled for each drug, and dimensionality reduction with PCA was applied and neighbours were calculated with a local neighborhood size of 5. Leiden clustering cluster with a resolution of 0. 3 was used to determine the final clusters. Differential expression analysis between drug response clusters was performed using limma  96  from normalized microarray expression values obtained from GSE107465. The moderated t-statistic for each gene was subsequently used as the rank statistic for GSEA analysis using Stem vs Mature Myeloid signatures from normal hematopoiesis (top 200 up-regulated and top 200 down-regulated genes from limma differential expression analysis of healthy LT-HSC vs Granulocyte/Monocyte sorted fractions from umbilical cord blood  33  ) and malignant hematopoiesis (top 200 correlated and top 200 anti-correlated genes with PC2).
Literature Screen for Drug-Treated RNA-seq Datasets To identify RNA-seq datasets collected from AML samples before and after drug treatment, Applying the search terms "Acute Myeloid Leukemia" and "AML" with the "Homo Sapien" and "RNA-sequencing" flags on Gene Expression Omnibus (GEO) and ArrayExpress, we identified 95 datasets posted before June 17, 2021. From these, 53 were inhibitors that met the inclusion criteria of human AML samples with available RNA-sequencing data collected before and after drug treatment. Datasets with only differential expression results or Bigwig files were excluded. Datasets with less than three samples in each treatment group were also excluded, resulting in a total of 47 datasets included in the final analysis. Detailed information on included datasets is available in Table  S12.
Each dataset was processed and underwent TPM normalization and deconvolution with CIBERSORTx using a signature matrix of seven leukemic cell types (LSPC-Quiescent, LSPC-Primed, LSPC-Cycle, GMP-like, ProMono-like, Mono-like, cDClike). For quality control among cell line samples, the deconvolution correlation values from each sample across every dataset were compared and the Jenks natural breaks algorithm was employed to identify cutoffs demarcating low, medium, and high correlation bins. Cell line samples classified as "low-correlation" with a correlation value below 0. 437 were excluded from further analysis, leaving 43 datasets spanning 153 treatment conditions.
Fedratinib and CC90009 -Classification of NPM1 Status in Primary Samples Patient samples from Princess Margaret Hospital (PMH) in Toronto were classified as NPM1mutant (NPM1-mut) or NPM1-wildtype (NPM1-wt) based on clinical sequencing results. For patient samples where targeted sequencing data was unavailable, we predicted NPM1 status using gene expression-based classifiers. First, log(TPM+1) normalized RNA-sequencing data from PMH samples and the three reference cohorts (TCGA, BEAT-AML, Leucegene) were combined and batch corrected using ComBat  88. Two groups of classifiers were trained: the first group comprised of Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) classifiers trained on NPM1 status from the reference cohorts and the second group comprised of LR, SVM, and RF classifiers trained on NPM1 status from 46 PMH samples for whom NPM1 genotype was available (out of 88 total samples).
LR, SVM, and RF classifiers were subsequently trained from these 63 genes and hyperparameter tuning was performed through a grid search with 10-fold crossvalidation for each model. Classification performance was evaluated by 10-fold nested crossvalidation with 100 repeats, yielding median accuracies of 0. 98 (LR), 1. 00 (SVC), and 0. 98 (RF). These models were subsequently used to predict NPM1 status in the remaining 42 PMH samples for whom NPM1 status was missing. For the final prediction of NPM1 status, the classifier with the lowest accuracy (group-1 LR) was excluded and the five remaining classifiers voted on the NPM1 genotype, with the majority vote being assigned as the final prediction. Together, this resulted in high-confidence predictions of NPM1 genotypes for patients in whom targeted sequencing data was not available.
Deconvolution of microarray data Last, we evaluated the performance of deconvolution applied to bulk transcriptomes profiled through microarray compared to bulk RNA-seq. Within the TCGA cohort, a majority of patients were profiled with both microarray and RNA-seq, and deconvolution was applied to both the microarray and RNA-seq profiles from these patients. Based on the correlation between the original and reconstructed transcriptomes for each patient, deconvolution from RNA-seq achieved high performance (median correlation = 0. 95) while deconvolution from Robust Multichip Average (RMA)  7  normalized microarray data performed poorly (median correlation = 0. 58). However, we found that an alternative single-sample normalization approach (SCAN) 8 attenuated the loss in deconvolution performance for microarray profiles (median correlation = 0.
75; Cycling LSPC, r = 0. 82) (Fig. S2J ). Thus, in the limited cases where deconvolution of microarray profiles was performed, SCAN normalized data was prioritized over RMA-normalization. To evaluate this, we first asked whether deconvolution using our new cell type annotations (Quiescent, Primed, and Cycling LSPC) can better discriminate between sorted AML fractions with and without functional LSC activity than deconvolution using the prior HSC/Prog annotations. To evaluate this, we built logistic regression and random forest classifiers to predict LSC activity using either the relative abundance of HSC-like and Prog-like populations (prior annotation) or of LSPC populations (new annotation) and measured their performance based on model AUC. To obtain high confidence estimates of model performance, we employed nested cross-validation and repeated this process for 1000 iterations, shuffling the data between each iteration (Fig.
441 gene sets from the MSigDB database corresponding to Hallmark genesets (n = 48), Oncogenic signatures (n = 182), Computationally-derived signatures (n = 667), Chemical and Genetic Perturbations (CGP) from prior Literature (n = 2112), GO Biological Pathways (4400), and previously published Immune Signatures (n = 5024). The relative expression of each signature was scored in each individual diagnosis and relapse sample (n = 88) through GSVA to generate a single-sample enrichment score for each signature. GSVA enrichment scores for each of the MSigDB signatures, alongside the inferred abundance of each leukemic cell type, were compared between diagnosis and relapse through paired t-tests based on the significance of their enrichment at relapse (absolute value of the log10(p)). Each signature was ranked and relapse enrichment of each leukemic subpopulation was subsequently compared against relapse enrichment of each of the MSigDB signatures.
Acknowledgments: We thank  C. Jones ,  A. Tikhonova ,  N. Iscove ,  S. Chan ,  P. van Galen ,  S. Abelson ,  B. Haibe-Kains ,  M. Anders , and all members of the  Dick laboratory  for valuable feedback on the manuscript. We thank  P. Valk  and the  HOVON-SAKK trial group  for providing the clinical annotations associated with GSE6891. A. G. X Zeng is supported by a  CIHR Vanier scholarship. J. E. Dick  is supported by funds from the:  Princess Margaret Cancer Centre Foundation ,  Ontario Institute for Cancer Research  through funding provided by the  Government of Ontario ,  Canadian Institutes for Health Research  ( RN380110 -409786 ),  International Development Research Centre Ottawa Canada ,  Canadian Cancer Society  (grant # 703212  (end date 2019), # 706662  (end date 2025)),  Terry Fox New Frontiers Program Project  Grant (Project#  1106 ),  University of Toronto's Medicine by Design initiative  with funding from the  Canada First Research Excellence Fund , a  Canada Research Chair, Princess Margaret Cancer Centre , The  Princess Margaret Cancer Foundation , and  Ontario Ministry of Health.
12 , 12 441 gene sets from the MSigDB database corresponding to Hallmark genesets (n = 48), Oncogenic signatures (n = 182), Computationally-derived signatures (n = 667), Chemical and Genetic Perturbations (CGP) from prior Literature (n = 2112), GO Biological Pathways (4400), and previously published Immune Signatures (n = 5024). The relative expression of each signature was scored in each individual diagnosis and relapse sample (n = 88) through GSVA to generate a single-sample enrichment score for each signature. GSVA enrichment scores for each of the MSigDB signatures, alongside the inferred abundance of each leukemic cell type, were compared between diagnosis and relapse through paired t-tests based on the significance of their enrichment at relapse (absolute value of the log10(p)). Each signature was ranked and relapse enrichment of each leukemic subpopulation was subsequently compared against relapse enrichment of each of the MSigDB signatures.
Background: Alzheimer's disease (AD) substantially increases health-related costs. This study investigates direct medical costs and characterizes the caregiver burden across AD stages. Methods: This study analyzed data from the French Primary Health Insurance Fund claims database and reflected this public payer perspective. Outpatients (N = 1998) visiting a memory clinic at Lyon University Hospital in France between 2014 and 2019 were included. Real healthcare costs (ie, ambulatory medicine, paramedical care, pharmaceutical treatment, public and private hospital stays, and medical transportation) were collected for patients 1 year prior to the date of the first memory visit and 2 years following the first visit (reference year: 2019). Patients were grouped based on a clinical diagnosis of cognitively normal with a subjective cognitive complaint (SCC), all-cause mild cognitive impairment (MCI), or AD dementia.
Study design and setting This is an ancillary study from the MEMORA cohort, which aimed to investigate the determinants associated with functional change in individuals receiving routine care  [11]. In the present study, the patients' clinical data were matched 1 year before and 2 years after the first memory consultation with claims data from the local branch of the PHIF (Caisse Primaire d' Assurance Maladie du Rhône [CPAM]) database for patients from the MEMORA cohort who underwent a consultation at a memory clinic of the Lyon University Hospital in France between 2014 and 2019. This claims database included all the direct medical costs taken in charge by the national healthcare system, as well as medical transportation  [12]. All patients had cognitive complaints, which were expressed by the patient or caregiver.
No signed consent was required for participation, but patients may refuse the use of their data for research purposes. Authorization for handling personal data was granted by the representative of the French Data Protection Authority, the Commission Nationale de l'Informatique et Libertés. All procedures are in compliance with the Declaration of Helsinki. The MEMORA cohort also obtained agreements from the regional ethics committee, Comité de Protection des Personnes Sud Est III and the French Data Protection Authority (Clini calTr ials. gov code: NCT02302482).
Primary outcome Primary outcomes included source of healthcare costs, components of claims data, valorization of the costs, and caregiver impact. First, healthcare costs, reflecting a healthcare payer perspective, were assessed for each care, act, and treatment that occurred for the insurance recipient and was reimbursed through the local branch of the PHIF. Claims data included all medical direct costs and the one nonmedical direct cost, medical transportation, which was supported by the local PHIF; PHIF is the equivalent of the national claims database described elsewhere  [13]. Second, components of claim data included (1) ambulatory medicine (e. g. , consultations and care provided by general practitioners, neurologists, psychiatrists, and geriatricians) and other ambulatory care (e. g.
Collection of data Baseline characteristics were collected at the initial visit. Clinical data were collected at the memory consultation by trained nurses and administrative staff and recorded in an electronic case report form. Cognitive performance was assessed using the MMSE. The total score ranges from 0 to 30, and a lower score indicates increased cognitive impairment  [15]. Functional abilities were evaluated using the Lawton Instrumental Abilities of Daily Living (IADL) scale; the summary score ranges from 0 to 8, with 8 indicating autonomy  [16]. The behavioral and psychological symptoms of dementia were assessed using the Neuropsychiatric Inventory (NPI); the total NPI score ranges from 0 to 144, and 144 indicates a severe behavioral disorder  [17]. Clinical diagnosis was determined by a neurologist, geriatrician, or psychiatrist based on clinical assessments and by using the MMSE score.
< 12 years), and treatment for comorbidities. Total costs were compared between groups by semester using generalized linear models (GLM) with gamma distribution and log link  [23]. As the nonmedical direct costs of medical transportation did not represent a large amount, these costs were added to the medical direct costs to calculate a total cost per patient. The sZBI scores were compared between groups using GLM. P values unadjusted and adjusted for age, gender, and education level were reported. Analyses were performed using Statistical Package for the Social Sciences (SPSS) version 19. 0 for Windows (SPSS Inc. , Chicago, Illinois, USA).
AD, Alzheimer's disease; MCI, mild cognitive impairment; Inf, limit inferior of the 95% confidence interval, SCC, subjective cognitive complaint; Sup, limit superior of the 95% confidence interval. (N = 1367; P <. 0001) Finally, when outlier patients with costs ≥ 3 SDs aboveaverage total costs were removed from the sensitivity analysis, we found that medical costs were numerically higher in the more severe AD dementia group, which may mean that extreme costs are related to medical conditions other than cognitive decline. Higher healthcare costs were observed in this study compared with those in the French GERAS cohort, which analyzed data from patients at memory clinics with mild, moderate, or moderately severe/severe AD dementia. These higher costs in the MEMORA cohort were observed even after accounting for differences in the years that the costs were reported  [37].
In the GERAS study, healthcare costs over 18 months were €5129 for mild AD dementia, €7106 for moderate AD dementia, and €8118 for moderately severe/severe AD dementia  [37]. In contrast, costs 1 year after the first visit to a memory clinic reported in our study were €10,558€ for mild AD dementia (€7336 in the sensitivity analysis), €10,544 for moderate AD dementia (€7315 in the sensitivity analysis), and €10,345 for moderately severe/severe AD dementia (€7834 in the sensitivity analysis). The MMSE was used to define the severity of AD dementia in both studies, and the mean age and mean MMSE scores at baseline were comparable for each level of disease severity, suggesting that these factors did not impact differences in costs. Given this, it is likely that the cost differences between the present study and GERAS cohorts differed because GERAS captured healthcare resources using the Resource Utilization in Dementia instrument, which is patient and caregiver reported, rather than analyzing data extracted from the PHIF claims database.
As typical in costs analyses, there were some patients who accumulated particularly high costs, resulting in discrepancies between means and medians. Skewed data was accounted for using gamma function in the models, and a sensitivity analysis was performed with statistically significant, high-costs outliers removed from the sample. Results of the sensitivity analysis showed that patients with extremely high costs were generally in poorer health and had a higher incidence of anxiety, which is consistent with previous studies  [41, 42]. As such, these factors help explain the elevated costs associated with these patients. In this study, direct medical and nondirect transportation costs were assessed using information from the PHIF claims database. To our knowledge, this is the first study to describe direct medical costs and medical transportation costs according to the severity of cognitive impairment in France from the perspective of the public payer and to report the costs before and after an initial memory clinic visit.
National health policies differ between countries and can change over time; in France, for example, reimbursement for drugs used to treat AD (donepezil, galantamine, memantine, and rivastigmine) ended in 2018. The health care that is not reimbursed by the PHIF may be reimbursed by private insurance or paid by the patients or their families. The neurocognitive diagnosis was determined at the first visit; thus, it is important to consider that the evolution of costs over time may have been accompanied by the decline of patients' health statuses, particularly cognitive and functional impairment in the AD dementia groups. It is less certain for patients in the SCC and the MCI groups, whose disease may not progress to a more advanced stage. Although the data did not allow us to show patients' health status decline, the increase in nursing home admissions and deaths over time supports this hypothesis (Supplementary Table  9 ).
Conclusions The economic toll of AD is debilitating and continues to grow. These results show that direct medical costs and caregiver burden rise with the severity of cognitive impairment from SCC to AD dementia; they were similar in the AD dementia groups that were determined by the MMSE. In AD dementia, the direct medical costs increased 2 years after the first consultation in a memory clinic. These results, in conjunction with data from other care components, will be critical in revealing the potential economic value of a therapeutic intervention that halts or slows the progression of AD.
The first component consisted of qualitative semi-structured interviews (Part 1), which were supplemented by an online survey (Part 2). In an independent quantitative analysis (Part 3), we utilized data from SUNFISH Part 2 (NCT02908685), a randomized, double-blind, placebo-controlled clinical trial, to conduct exploratory post-hoc anchor-and distribution-based analyses to estimate thresholds for meaningful change from a patient-centered perspective.
The meaningful change interviews were conducted between February 2020 and May 2020. Interviews were audio recorded and transcribed verbatim. Data were subjected to thematic analysis, a process by which researchers review interview transcripts to identify, analyze and interpret patterns (or "themes") within qualitative data by assigning "codes, " which was facilitated using ATLAS. Ti software  (16). The study was approved by Copernicus Group IRB (20192527) and written informed consent was obtained from participants aged ≥18 years old and written informed consent was provided by caregivers for individuals aged 12-17 years old prior to the interviews, as well as obtaining assent from adolescents. Participants were reimbursed for their time in line with US fair market value for interview research.
The online survey was completed by participants between January to April 2020. It was conducted following market research principles and hence did not receive ethical approval but was consistent with British Healthcare Business Intelligence Association Adverse Event Reporting in Market Research as well as General Data Protection Regulation guidelines and guidelines set by ESOMAR and EphrMRA for all European research. In addition, data checks (e. g. , avoidance of duplicate participants) were in place to ensure data integrity. A tick box indicating consent to participate was included in the survey prior to completion. Assent was provided by caregivers prior to survey completion for individuals aged <18 years old. Upon completion of the survey, participants were reimbursed for their time in line with geographical fair market value rates for survey research which was not linked to the patient or caregiver survey responses.
Part 3. Quantitative Analyses Using the Validated MFM32 Assessment Analyses were performed using MFM32 data from Part 2 (up to 52 weeks) of the SUNFISH clinical trial (N = 180 target sample). The SUNFISH study is a multicenter, randomized, double-blind, placebo-controlled, Phase 2/3 study to assess the safety, tolerability, pharmacokinetics, pharmacodynamics, and efficacy of risdiplam in a broad adult and pediatric population aged 2-25 years old with Type 2 and non-ambulant Type 3 SMA, including patients with contractures and scoliosis. Refer to Mercuri et al. (17)  for methods and results of the SUNFISH Part 2 clinical trial. For this research, the risdiplam and placebo 52-week data from SUNFISH Part 2 were pooled as the aim of this analysis was to define a meaningful score change on the MFM32 for future research rather than to evaluate treatment efficacy  (18, 19).
DISCUSSION This study represents a comprehensive multi-pronged approach to evaluating individual patient-level meaningful change assessed using a patient friendly version of the MFM32 (Parts 1 and 2) and the validated MFM32 (Part 3) in individuals with Type 2 and Type 3 SMA. The combined qualitative and quantitative approaches represent endorsed methods, ensuring inclusion of the patient voice via the collection of interview and survey data using patient-friendly versions of the MFM32  (14, 32)  as well as traditional quantitative methods using the validated MFM32 clinical assessment  (11). The individuals with SMA and caregivers enrolled in the qualitative interview and survey sample confirmed that stability on the concepts assessed in the patient-friendly MFM32 over 1 year is a meaningful outcome across SMA types and functional status (i.
From a quantitative perspective, there is no existing anchor-based meaningful change estimate on the MFM32 in SMA. However, previous anchor-based estimates of meaningful change on the MFM32 in congenital muscular dystrophy using a self-reported global item found that 2. 5 points was a withinpatient meaningful improvement  (38). This result is broadly consistent with the findings in this research. While not patient centered in nature, the distribution-based SEM estimate also converged on ∼3 points as a meaningful change on the MFM32. More variable estimates from the 0. 5 and 0. 2 SD methods were observed. Previous distributionbased estimates in SMA using a real-world dataset found 3-4 points as an appropriate threshold  (Trundell et al. , 2019 ; poster presented at Cure SMA). Of note, distribution-based estimates have previously formed the basis for estimations of meaningful change for motor function scales in SMA such as the Hammersmith Functional Motor Scale Expanded and RULM  (39).