dataesr/scientific-paragraphs-categorization huggingface dataset:
Luminal-C cells are a minor population in control prostates, and the predominant epithelial subset in Pten-deficient prostatic lesions  (Abu El Maaty et al, 2021 , 2022). Cells with similar characteristics have been recently suggested to be intrinsically castration-resistant  (Baures et al, 2022). Interestingly, the abundance of luminal-A but not luminal-C cells was reduced after castration  (Figs 1D and EV1F and H). These data therefore demonstrate that only a minor population of prostatic luminal cells is eliminated by castration of Pten (i)peÀ/À mice, and indicate intrinsic castration-resistant properties for luminal-C cells. Differential expression analysis revealed that the transcript levels of around 380 genes were altered in luminal-C cells by castration (Dataset EV1). As expected, enrichment analysis of the genes downregulated in luminal-C cells of castrated mice compared with those of sham-operated ones using the Molecular Signatures Database (MSigDB) identified "androgen response" among the top 10 downregulated pathways (Fig 1E ; Table  EV1 ), with the expression of a number of androgen-responsive genes, including Pbsn, Tgm4, and Fkbp5, reduced by androgen deprivation  (Fig 1F; Dataset EV1).
On the other hand, enrichment analysis of the genes upregulated in luminal-C cells of castrated mice identified inflammation-related pathways, including "TNFA signaling via NF-kB" and "IL2-STAT5 signaling"  (Fig 1G; Table EV1). A number of genes encoding cytokines, including Il1b, Cxcl1, and the MDSC-recruiting factor Cxcl5, were induced by castration (Fig 1H; Dataset EV1), the latter potentially underscoring the enrichment of MDSCs in prostatic tumors of castrated Pten (i)peÀ/À mice  (Figs 1I and EV1F). Interestingly, enrichment analyses also identified "Hypoxia," a major feature of Pten-deficient prostatic lesions, among the hallmarks upregulated in luminal-C cells of castrated mice (Fig 1G ;.
◀ Figure  2. Effects of castration on prostates of Pten/Hif1a (i)peÀ/À mice. A Prostate weight of control (Pten L2/L2 /Hif1a L2/L2 ) and Pten/Hif1a (i)peÀ/À mice sham or CTX at 3 months AGI and analyzed 1 month after surgery. N = 5-18 mice/ condition. Data presented are mean AE SEM. P-values were determined by one-way ANOVA followed by a post hoc Tuckey test. B Representative H&E staining of DLP of sham and CTX Pten/Hif1a (i)peÀ/À mice. Scale bars: 100 lm. N = 3 mice/condition. C Gland areas in DLP of sham and CTX control and Pten/Hif1a (i)peÀ/À mice. Five glands per mouse were quantified. N = 3 mice/condition. Data presented are mean AE SEM. P-values were determined by one-way ANOVA followed by a post hoc Tuckey test. D Histological scoring of DLP of sham and CTX Pten/Hif1a (i)peÀ/À mice.
N = 3-5 mice/condition. Five glands per mouse were quantified. Data presented are mean AE SEM. P-values were determined by one-way ANOVA followed by a post hoc Tuckey test. I Western blot analysis of CC3 in protein lysates of organoids generated from prostates of Pten (i)peÀ/À and Pten/Hif1a (i)peÀ/À mice at 3 months AGI and treated with enzalutamide (Enz) for 24 h at the indicated concentrations. b-actin was used as a loading control. Data are representative of two independent biological replicates. Source data are available online for this figure. Furthermore, our study demonstrates that inhibiting HIF1A relieves castration resistance even after castration-induced plasticity of luminal cells. Prostatic tumors of mouse models based on the prostate-specific deletion of Pten have been previously shown to be castrationresistant  (Wang et al, 2003; Mulholland et al, 2011).
Data presented are mean, and error bars correspond to SEM. N = 3-5 mice/condition. P-values were determined by an unpaired t-test. H Gland areas of DLP of sham and CTX Pten (i)peÀ/À mice, treated with vehicle or PX-478, as depicted in (E). N = 3-4 mice/condition. Data presented are mean AE SEM. Five glands per mouse were quantified, and P-values were determined by one-way ANOVA followed by a post hoc Tuckey test. Source data are available online for this figure. Our findings support the results of  Calcinotto et al (2018) , who showed that treating prostate cancer mouse models with a CXCR2 antagonist decreases the number of MDSCs and prevents the progression to CRPC. However, our data demonstrating apoptotic activation by enzalutamide in organoids generated from prostates of Pten/Hif1a (i)peÀ/À mice but not in those generated from Pten (i)peÀ/À ones show that cell-autonomous mechanisms play an important role in the enhanced sensitivity to AR signaling blockade by HIF1A inhibition.
Histological examination Five micrometre sections were prepared from paraffin-embedded prostates and dried overnight at 37°C. Hematoxylin and eosin (H&E) staining was performed on prostate sections according to standard protocols. Slides were scanned in brightfield mode using the Nano-Zoomer digital slide scanner (Hamamatsu) and analyzed using the NDP. view2 Viewing software (Hamamatsu). Representative images of the DLP are provided. Histopathological assessment was performed on the DLP. The number of glands with a normal architecture, PINs, and adenocarcinomas was determined by a resident in oncology, as described  (Shappell et al, 2004). The area of at least five representative glands of the DLP was determined in each sample using the Qu-Path software  (Bankhead et al, 2017).
scRNA-seq and data analysis Cell number and viability of FACS-sorted cells were performed with trypan blue exclusion assay, as described (Abu El Maaty et al, 2022). Cells with a viability of > 95% were processed using the Chromium Controller (10× Genomics, Leiden, The Netherlands). Ten thousand cells were loaded per well to capture between 5,000 and 10,000 cells in nanoliter-scale Gel Beads-in-Emulsion (GEMs). Libraries were generated and sequenced on Illumina NextSeq 550 as 100 bases paired-end reads, as described  (Abu El Maaty et al, 2022). RA 2. 7. 7 and Cell Ranger 3. 0. 2 mkfastq were used for image analysis, base calling, and demultiplexing. Cell Ranger 6. 0. 2 count and the mouse reference 2020-A (GRCm38 Ensembl release 98 and Gencode release M23) were used for alignment, barcode, and UMI filtering and counting.
Seurat R package  (Butler et al, 2018)  version 4. 0. 5 was used to perform the single-cell analysis. Briefly, for each sample, cells expressing more than 8,000 genes, < 200 genes, and with a percentage of mitochondrial reads > 30% were discarded from the analysis. Sample counts were then normalized using LogNormalize method before being integrated together with anchors found using the 20 first dimensions. Cells expressing Pate4 (normalized count > 4) were discarded from the analysis, as they have been previously reported to be epididymal contaminants  (Abu El Maaty et al, 2021 , 2022). The resulting integrated object was then scaled, and a UMAP was constructed using the 20 first principal components of a PCA analysis. Cell clustering was performed using FindNeighbors and FindClusters (with a resolution of 0.
Figure3. Long-term effects of castration on prostates of Pten/Hif1a (i)peÀ/À mice. A Prostate weight of control (Pten/Hif1a L2/L2 ) and Pten/Hif1a (i)peÀ/À mice, sham or CTX at 3 months AGI and determined at 5 months after surgery. N = 4-9 mice/ condition. Data presented are mean AE SEM. P-values were determined by one-way ANOVA followed by a post hoc Tuckey test. B Representative H&E staining of DLP of Pten/Hif1a (i)peÀ/À mice, sham and CTX at 3 months AGI and analyzed at 5 months after surgery. N = 4-5 mice/condition. Scale bars: 100 lm. C Gland areas in DLP of control and Pten/Hif1a (i)peÀ/À mice, sham and CTX at 3 months AGI and analyzed at 5 months after surgery. N = 3-4 mice/condition. Five glands per mouse were quantified. Data presented are mean, and error bars correspond to SEM. P-values were determined by one-way ANOVA followed by a post hoc Tuckey test.
Acknowledgements We thank the IGBMC mouse, cell culture and imaging facilities, the flow cytometry facility members  C. Ebel  and  M. Phillips , the ICS histopathology facility and GenomEast, and a member of the " France G enomique" consortium  ( ANR-10-INBS-0009 ) for excellent assistance. We are grateful to  G. Laverny  for helpful discussions. This study was supported by funds from the  Centre National de la Recherche Scientifique (CNRS)  (D. M. ),  Institut National de la Sant e et de la Recherche M edicale (INSERM)  (D. M. ),  Universit e de Strasbourg  (D. M. ),  Interdisciplinary Thematic Institute IMCBio , as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS  and  Inserm , supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ), and by  SFRI-STRAT ' US  project ( ANR 20-SFRI-0012 ) and  EUR IMCBio  ( ANR-17-EURE-0023 ) under the framework of the  French Investments for the Future Program  (D.
M. ),  Fondation pour la Recherche M edicale  grant  EQU201903007800  (D. M. ),  ITMO Cancer  within the funds of the Cancer Plan 2014-2019 (administrated by INSERM) and with a collaborative support from the  French National Cancer Institute  (D. M. ),  INCa -PLBIO 2021-074  (D. M. ),  Fondation ARC pour la Recherche sur le Cancer  (PGA-2022-www. fondationARC. org) (D. M. ),  Agence Nationale de la Recherche  grant  ANR-10-LABX-0030-INRT  under the  frame program Investissements d'Avenir labeled   ANR-10-IDEX-0002-02  (D. M. ),  Fondation ARC pour la Recherche sur le Cancer  (M. A. A. e. M. ),  ITMO Cancer Aviesan/Inserm/Cancer 2020  through the " Formation a la recherche fondamentale et translationnelle en canc erologie" program  (J. T. ),  Al Bizri Foundation (R. E. B. ). A CC-BY public copyright license has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission, in accordance with the grant's open access conditions.
Average log2FC: log fold change of the average expression between the two groups. Positive values indicate that the gene is more highly expressed in the first group. Pct. 1: percentage of cells where the gene is detected in the first group. Pct. 2: percentage of cells where the gene is detected in the second group. P val adj: Adjusted P-value, based on Bonferroni correction using all genes in the dataset. Gene Average name P-value log2FC pct. 1 pct. 2 P val_adj Cp 1. 55E-34 2. 229 0. 595 0. 343 3. 30E-30 Car12 2. 42E-32 0. 813 0. 409 0. 157 5. 17E-28 Zfp36 9. 50E-15 0. 754 0. 850 0. 807 2. 03E-10 Bhlhe40 2. 77E-14 0. 640 0. 668 0. 592 5. 91E-10 Map3k1 3. 07E-13 0. 403 0. 598 0. 518 6. 54E-09 Pfkfb3 3. 57E-13 0. 473 0. 346 0. 209 7. 61E-09 Fos 7. 68E-10 0. 594 0. 904 0. 851 1. 64E-05 Errfi1 4.
| MRI data preprocessing Preprocessing was performed using MELODIC (Multivariate Exploratory Linear Optimized Decomposition into Independent Components) version 3. 14, which is part of FMRIB's Software Library (FSL, http://fsl. fmrib. ox. ac. uk/fsl). The preprocessing consisted of the following steps: the first five functional images were discarded to reduce scanner inhomogeneity, motion correction was performed using MCFLIRT, non-brain tissue was removed using BET, intensity was normalized, temporal band-pass filtering with sigma 100 s was performed, spatial smoothing was applied using a 5 mm FWHM Gaussian kernel, rigid-body registration and single-session ICA with automatic dimensionality. Then noise components and lesion-driven artifacts (e. g. , head movement, metal, and physiological noise artifacts) were manually regressed out for each subject.
Then, the T1-weighted image was co-registered to the standard MNI space by using FLIRT (12 DOF), and FNIRT. This transformation matrix was inverted and applied to warp the resting-state atlas from MNI space to the singlesubject functional data. Finally, the time series for each of the 214 regions brain areas were extracted using custom-made Matlab scripts using "fslmaths" and "fslmeants. ".
, 2021; Gilson et al. , 2016). In particular, the optimization procedure iteratively adjusts the pair-wise connection strengths (weights of connectivity matrix A) and the levels of covariances of the Gaussian noise injected at each region (diagonal entries of matrix B). Thus, EC is the weighted connectivity matrix A' that results at the end of the iterative optimization process. Estimation of MOU-EC was carried out using the pyMOU Python package, see the "Data availability statement. " In this study, individual EC matrices were estimated for all HC participants, patients with UWS and partially recovered patients in MCS. A limitation of EC estimation for the whole-brain is the large number of free parameters to be fitted, namely, the n 2 entries of the connectivity matrix A and the n 2 entries for the covariances of the Gaussian white noise inputs.
Namely, the connectivity matrices A were replaced by the EC matrices previously computed for each subject and the relaxation time constants τ i were precomputed from the BOLD signals of the brain regions. The benefit of doing so is that the complete analysis and modeling pipeline is based on the same model, assumptions and limitations, thus allowing for a coherent interpretation of the results at every step  (Gilson et al. , 2020). All calculations were carried out using the NetDynFlow Python package, see the "Data availability statement. ".
| Metabolic index using [ 18 F]FDG-PET Alongside the MRI analyses, we have estimated the cerebral glucose metabolism by means of the metabolic index for the best-preserved hemisphere (MIBH) using [ 18 F]FDG-PET, that shows high accuracy to discriminate between UWS and MCS patients. To this end, data acquisition was performed with a Gemini PET-CT (Philips Medical Systems) while receiving a bolus intravenous injection of FDG (185-370 MBq). The data was recorded in a single 12-minute emission frame after a 30-minute uptake phase and the images were corrected for attenuation  (Annen et al. , 2016; Stender et al. , 2016). The following steps were followed to calculate MIBH  (Stender et al. , 2015 (Stender et al. , , 2016)). First, we created a population-specific template for the patients (by taking all DoC patients) and control groups to overcome the problem of big deformations due to brain lesions and atrophy by following the approach of previous studies  (Annen et al.
The whole brain network response was calculated as the average network response across all the brain regions. An ANOVA with Tukey post hoc comparison, Bonferroni corrected for 200 time points of integration, was employed. Before assessing the local broadcasting and receiving differences, we first evaluated the effect of etiology (anoxia and TBI) versus diagnosis (UWS and MCS) for whole brain early peak responses (i. e. , the maximum amplitude of the whole brain network response curve) and late whole brain network responses (i. e. , from integration time 60-200 s [modeled time], based on the findings of the ANOVA described above) using a linear regression model as implemented in MATLAB (i. e. , fitlm function). Both the mean effects for diagnosis and etiology were considered as well as their interaction.
ACKNOWLEDGMENTS We would like to thank the healthy participants, the patients, their families, caregivers and treating clinicians for their participation in this study. The authors thank the whole staff from the  ICU and Nuclear Medicine departments, University Hospital of Liège. We are highly grateful to the members of the  Liège Coma Science Group  for their assistance in clinical evaluations. Coma Science Group Collaborators:  Alice Barra ,  Charlotte Martial ,  Claire Bernard ,  Estelle Bonin ,  Emilie Szymkowicz ,  Jean-Flory Luaba Tshibanda ,  Leandro Sanz ,  Marie Vitello ,  Roland Hustinx.
DATA AVAILABILITY STATEMENT Data employed in this manuscript can be made available by the corresponding author on reasonable request. Regarding code, intrinsic ignition was calculated using MATLAB code available at https://github. com/RajanikantPanda/Ignition_and_Information_flow_for_DOC, estimation of effective connectivity was computed using the pyMOU Python package (https://github. com/mb-BCA/pyMOU) and the study of responses to exogenous perturbations was carried out using the NetDynFlow package in Python (https://github. com/mb-BCA/NetDynFlow). Both GitHub repositories for pyMOU and Net-DynFlow contain usage tutorials in the form of Jupyter Notebooks. Usage of NetDynFlow and pyMOU functions in Python is available at https://github. com/RajanikantPanda/Ignition_and_Information_ flow_for_DOC.
Bone mineral density in adult patients with pyruvate kinase deficiency on long-term mitapivat treatment Pyruvate kinase (PK) deficiency is a rare, hereditary, chronic hemolytic anemia that is associated with serious complications including reduced bone mineral density (BMD). A recent study revealed that low BMD is highly prevalent in patients with PK deficiency pretreatment. Here, we report the first large-cohort analysis to systematically evaluate BMD over time in patients with PK deficiency receiving long-term mitapivat treatment, using data from clinical studies (clinicaltrials gov. Identifiers: NCT02476916, NCT03548220, NCT03559699, NCT03853798). In our analysis, BMD remained stable or improved in the majority of patients for up to 5. 9 years, regardless of age. Given our findings, mitapivat may halt bone loss by decreasing hemolysis, improving erythropoiesis, and stabilizing iron homeostasis in patients with PK deficiency.
10  This highlights the need to assess the impact of long-term mitapivat treatment on BMD. We report BMD over time in patients (≥18 years old) with PK deficiency receiving long-term mitapivat (>12 months), using pooled data from the phase II DRIVE-PK  13  (clinicaltrials gov. Identifier: NCT02476916) study, phase III ACTIVATE 10 (clinicaltrials gov. Identifier: NCT03548220) and ACTIVATE-T 11 (clinicaltrials gov. Identifier: NCT03559699) studies, and long-term extension (LTE) study (clinicaltrials gov. Identifier: NCT03853798) (Online Supplementary Figure  S1 ). Baseline DXA scans were taken at screening for ACTIVATE and ACTIVATE-T, and at screening or up to 3 months before the first dose for DRIVE-PK. DXA scans were performed every 24 weeks during mitapivat and performed locally for all three studies.
LETTER TO THE EDITOR on BMD are unknown. Finally, while patients were treated and followed up for up to 5 years, most were treated for a shorter period, and progression of BMD abnormalities usually occurs over years in the general population and in patients with thalassemia syndromes. 15, 16  While the stability observed here suggests against any major negative impact of potential mitapivat-associated aromatase inhibition on BMD, this needs to be further investigated over a longer time period. BMD will continue to be monitored as part of the ongoing LTE study of mitapivat in adult patients with PK deficiency and is being assessed in two ongoing studies in children (<18 years) with PK deficiency (clinicaltrials gov. Identifier: NCT05175105, NCT05144256). Given the low baseline BMD in patients with PK deficiency, it is hypothesized that mitapivat may halt bone loss through its mechanism of action by decreasing hemolysis, improving erythropoiesis, and stabilizing iron homeostasis.
In support of this mechanism, findings from a study in patients with αor β-non-transfusion-dependent thalassemia receiving mitapivat have also shown that BMD remains stable over time through 84 weeks. 17 BMD will continue to be monitored as part of the ongoing LTE study of mitapivat in adult patients with PK deficiency and is being assessed in two ongoing studies in children (<18 years) with PK deficiency (clinicaltrials gov. Identifier: NCT05175105, NCT05144256).
Performance assessment A B S T R A C T The systematic use of patient-reported measures (PRMs) [i. e. , patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs)] is advocated as an effective way to improve care practices. However, whether PRMs can lead to the performance assessment of healthcare organisations (HCOs) through valid quality indicators (QIs) for national purposes (i. e. , public reporting and paying for performance) is open to debate. This study undertakes a scoping review to examine the use of PRMs as QIs for health policy purposes and to identify the challenges faced in the emblematic case of oncology. According to PRISMA guidelines, published papers, websites and reports published by national and international initiatives were analysed using five online databases (Web of Science, Scopus, PubMed, JSTOR and Google Advanced Search), and then studied using the same keywords.
We selected 61 articles and 19 websites/reports and identified 29 PREMs and 48 PROMs from 14 countries and two international initiatives that routinely used them as QIs for HCOs' comparisons. Four types of barriers to this specific use were identified relating to the definition of a standard set, scientific soundness, data collection, and the actionability of such measures. Despite current developments, different barriers still must be overcome before PRMs can be used for health policy purposes in oncology. Future research is needed to ensure that valid QIs related to PRMs are applied at a national level.
PREMs assess patients' perceptions of their care experience, including organisational features (e. g. , the information provided by doctors and nurses), feelings (e. g. , attention to pain), and empiricalbased aspects of their process of care (e. g. , waiting time during appointments). Generic PREMs address any patient, while specific PREMs only address patients with a specific disease. Accompanying these developments, four topics for using PRMs have emerged  [8, 9] : (i) Clinical trials. PRMs represent a cost-effective data collection system providing invaluable evidence of the impact of a new treatment or intervention for patients' symptoms, status, and quality of life. (ii) Patient/healthcare professional relationships. PRMs may be helpful in the monitoring and assessment of various treatments.
However, their developments require solving numerous implementation issues because PRMs need to be adapted as QIs for assuming reliable comparisons between HCOs. For such use, a QI PRO-related (e. g. , a standardised, evidence-based measure of health quality)  [12]  has to demonstrate metrological qualities (i. e. , the instrument measurement qualities), standardised and routinely feasible data collection, and an ability to be actionable for improvements. As such, the use of PRMs for supporting health policy purposes represents an extreme case to understand how technical, social, and cultural challenges may be overcome  [13]. To better understand the current use of PRMs for supporting health policy purposes, we conducted a scoping review to systematically map the initiatives in this area and identify any existing knowledge gaps in the provision of validated quality measurements, and in any other challenges they may face in their implementation.
To identify potentially relevant articles (step 2), the following bibliographic databases were searched from January 2006 to July 2021: Web of Science, Scopus, PubMed, and JSTOR. This period was selected as it was a time when PRM research was rapidly expanding, and there was beginning to be enough hindsight to identify the challenges that pioneering initiatives were facing. The analysis was supplemented by using Google Advanced Search to select websites and official reports of HCOs and national and international health authorities over the same period (regardless in this last case of language -English, French, and Spanish). A Boolean search strategy was used to identify the following keywords in the text of articles/ reports/websites: (1) (Patient-Reported Outcome Measure or Patient-Reported Experience Measure or Patient-Reported Outcome) AND (2) cancer AND (3) Quality indicator.