dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors would like to thank  Noe ¨mi Ku  ¨ng for help with experiments, Francoise Ischer and  Danielle Brandalise  for technical help with generating C. albicans mutants;  Selene Mogavero  for assessing Ece1 peptides in ECE1-overexpressing mutants;  Ilse Jacobsen  for sharing the KOH protocol; the  Laboratory Animal Service Center of University of Zu  ¨rich for animal care; the  Laboratory Animal Model Pathology of University  for histology; the  Lausanne Genomic Technologies Facility of University of Lausanne  for RNA sequencing; and members of the  Lei-bundGut-lab  for discussions.
Funding: CL was funded by a grant from the  Swiss National Science Foundation  to SLL (grant  N3 10030_166206 , www. snf. ch). KMSV was funded by a grant from the  Swiss National Science Foundation  to SLL, SD and  CdE  (grant  NC RSII5_173863 , www. snf. ch). SA and SAV were funded by a grant from the  Swiss National Science Foundation  to DS, MP and SLL (grants  N3 10030_166206 ,  CRSII5_173863  and  CRSII3_141848 , respectively, www. snf. ch). RFM was funded through the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie action, Innovative Training Network  (FunHoMic; grant  N˚812969 , www. funhomic. eu),to CdE and SLL. LSR's postdoctoral fellowship was funded through a  Fondation pour la Recherche Medicale grant  to CdE (FRM,  DBF20160635719 , www.
frm. org/). SIB partly funded by the  Swiss Federal Government  through the  Federal Office of Education and Science  (https://www. bfs. admin. ch/bfs/en/home/statistics/education-science. html). Work in CdE's laboratory  is supported by a grant from the  Agence Nationale de la Recherche  ( ANR-10-LABX-62-IBEID ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Over the past 40 years, a steady increase in the age-adjusted incidence rate of iCCA has been observed regardless of the geographical area considered. In the United States, the incidence has increased in 40 years from 0. 44 to 1. 18 per 100,000 in 2012, i. e. , an increase of 2. 3% per year and even 4. 36% per year over the last ten years. However, these trends need cautious interpretation, given that all versions of the main International Classifications of Diseases (ICD) have failed to include a separate code for the largest group of CCA, and previous versions of ICD have cross-referenced pCCA to iCCA  [8]. The decrease in the incidence of eCCA can be explained by the fact that in the databases, cancers of the gallbladder were included in the eCCA groups. However, over the past 30 years, there has been an increase in the rate of cholecystectomy and therefore a decrease in gallbladder cancers  [4].
However, some limitations restrict their clinical applicability. None of them specified whether the dataset used for training and testing the algorithm included routine clinical cases, nor how the scans were selected. When the evaluation of the model on a holdout test dataset was provided, the number of test scans was always less than 30. Moreover, none of the algorithms in the studies were used to segment all the structures of interest for computer-assisted planning of orthognathic surgery and personalized implant manufacturing, and only one work segmented bone and teeth separately  [18]. This calls into question the reproducibility and generalizability of previously published results, which are crucial factors for clinical validity  [7, 20]. Recently, the nnU-Net framework was proposed as an out-of-the-box tool which automatically configures itself in order to perform deep learning-based biomedical image segmentation  [21].
This tool is publicly available and was shown to surpass most existing models on 23 public datasets used in international biomedical segmentation competitions. nnU-Net could be helpful for direct clinical applications, as it is open source and does not necessarily require expert knowledge to obtain competitive results. To our knowledge, the performance of this tool has not yet been evaluated for the segmentation of craniofacial hard tissue in a clinical context. In this work, our main objective was to evaluate the performance of the nnU-Net framework for automatic segmentation of CMF structures in routine CT scans performed for computer-assisted orthognathic surgery.
Patient selection Data were selected from a retrospective cohort of all consecutive patients having undergone orthognathic surgery in a single maxillofacial surgery department between January 2017 and December 2019. Patients referred to this center presented a wide variety of dentofacial deformities, came from various socioeconomic backgrounds and were ethnically diverse. Patients were considered for inclusion whatever dental deformity they presented, with no minimum age. Exclusion criteria were refusal to participate in the research (all patients were contacted by mail) and lack of industry-certified CT scan segmentations (see next paragraph: "Ground truth segmentation process"). Of the 473 subjects who underwent orthognathic surgery within the study timeframe, 4 refused to participate and 16 lacked industry-certified CT scan segmentations.
453 subjects (453 CT scans) were eventually included in our dataset (Fig. 1 ). Mean in-plane pixel size of the CT scans was 0. 45*0. 45mm 2   and their mean slice thickness was 0. 34mm. Most CT scans (n = 417) were obtained using a GE Healthcare Discovery (GEHC) CT750HD scanner with a tube current of 50mA, an exposure time of 730s and a tube voltage of 100kV. Scans were randomly distributed among a train/validation set (n = 300) and test set (n = 153). CT scans characteristics and CT machines are detailed in Table  1. This study was ethically approved by an Institutional Review Board.
Public mandible test dataset In order to assess the generalizability of our trained model, we tested it on a public dataset of 10 highresolution CT scans from clinical practice  [22]. These scans had the particularity of presenting complete mandibular bone structures entirely devoid of teeth. Ground truth manual segmentations of the mandible by two experts (A and B) were provided, which differed from our segmentations in that they were filled. Table  1  provides more details about this dataset. To the best of our knowledge, this is the only publicly available dataset of high-resolution Head CT scans with ground truth segmentation of one of the structures of interest for orthognathic surgery planning.
nnU-Net framework The nnU-Net deep learning framework was used as an out-of-the-box tool, following instructions given by Isensee et al. [21]. Our raw train/validation dataset was used to automatically configure preprocessing, network architecture, training and post-processing pipelines. No modifications were made in setting the nnU-Net hyperparameters and data augmentation strategy. The training of 3D full resolution U-Net was performed on our train/validation set according to a 5-fold cross-validation strategy. After the end of the training pipeline, cross-validation result analysis showed that the model incorrectly labeled a few voxels as teeth in some scans displaying no upper and/or lower teeth. As a result, we implemented additional post-processing for teeth masks, consisting in removing all components smaller than an empirically-determined threshold.
Statistical analysis Continuous variables are presented as mean ± standard deviation and categorical variables are expressed as numbers and percentages. vDSC and sDSC results are presented as percentages (%). The Wilcoxon test was used to compare vDSC and sDSC in scans obtained with GEHC CT750HD and scans obtained with other machines; p-values < 0. 05 were considered significant. All data were analysed with Python (v. 3. 7) and RStudio software (v. 1. 3).
Quantitative evaluation on our test dataset The mean results of vDSC and sDSC for each segmentation label of our test set are shown in Table  3 , while Figure  2  shows the distribution of the results. The mean vDSC for all masks was 92. 24 ± 6. 19%. Without the mandibular canal results, the mean vDSC was 94. 90 ± 0. 91%. The mean sDSC for all masks was 98. 03 ± 2. 48%. Out of the 153 scans, 148 presented a mean sDSC for all masks which cleared the 95% limit for clinical significance. There were no statistically significant differences in both vDSC and sDSC when comparing scans obtained with GEHC CT750HD and scans obtained with other machines. Additional quantitative evaluation (Jaccard Coefficient, Volumetric Similarity, Average Surface Distance and Hausdorff distance) results for cross-validation and test datasets are provided in Supplementary Materials.
Four subjects representative of our test dataset and of the anatomic diversity of the database were chosen to illustrate our results (Fig. 3 , Fig. 4 , Supplementary videoclips 1 to 4). The most notable segmentation error made by the model on these subjects was the incorrect labeling of an upper deciduous tooth as a lower tooth in a patient suffering from craniofacial syndrome with several impacted teeth (Fig. 3b , Fig. 4b  and Supplementary videoclip 2).
Expert evaluation on our test dataset The results of the industry expert validation process are shown in Table  4. Validation rates were about 90% for mandible and mandibular canals, 80% for upper skull and 60% for teeth. In total, 19 (42. 2%) CT scans had all their segmentation masks validated. When excluding dental masks, 34 (75. 6%) CT scans had their segmentation masks validated. Three CT scans out of the 45 (6. 7%) did not show any metallic artefact, and had their 5 segmentation masks validated by industry experts. Comments appended to non-validated cases for bones mentioned small holes on the bony surface or undersegmentation of the anterior nasal spine. Reasons for rejection of mandibular canal masks were the inclusion of a few outlier voxels or the omission of an auxiliary canal during segmentation.
As to teeth, reasons for non-validation were under-or over-segmentation of a few voxels of the apex (Fig. 5 ). Quantitative evaluation on public mandible dataset vDSC and sDSC results of the inference on the public mandible dataset are provided in Table  5. Most of the model's errors were located in the anterior part of the mandible, where the edentulous patients' alveolar bone was atrophic and extremely thin (Fig. 6 ). One scan (mandible #10), performed on a subject with an endotracheal tube, produced low-quality results. Additional quantitative evaluation results for this dataset are presented in Supplementary Table  3.
Discussion The main goal of this study was to evaluate the performance of the nnU-Net framework for semantic segmentation of CMF CT scans obtained for the planning of orthognathic surgeries. We chose this deep learning framework because it is open source and well-maintained, and has the ability to automatically configure itself without manual intervention. It has delivered state-of-the art segmentation results on a large diversity of biomedical datasets, surpassing most highly-specialized algorithms  [21]. Our quantitative results demonstrated the model's relevance for CT scan segmentation, with 97% of our test dataset showing a mean sDSC above 95%. However, this study also illustrated the challenges arising from the evaluation of deep learning-based algorithms in the case of demanding, specific clinical applications which forbid blind trust in quantitative metrics  [7, 23].
However, many other clinical applications, such as computer-assisted diagnosis or planning not involving personalized implant manufacturing, would not require such precision in the segmentation of dental apices. Finally, the clinical value of sDSC metrics in the context of small object segmentation was demonstrated by our mandibular canal segmentation results, for which vDSC did not seem like an appropriate metric  [7, 23]. This study, which followed best practice guidelines  [20] , is the first to train and test a deep learning segmentation model on such a large number of high-resolution CMF CT scans. Our results are comparable or superior to those of previously published studies, despite the more challenging task we faced: all previous results were based on smaller test datasets (between 0 and 30 scans) and fewer segmentation masks  [5, [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18].
No previous work had included a cohort of consecutive patients, and only one previous publication had clearly stated that its database included patients with syndromic conditions  [16]. Our results for the segmentation of scans from patients with marked syndromic deformities show the versatility of the model (Fig. 3b ), and are comparable to those of Wang et al. who recently reported on a deep learning-based model for multi-task segmentation of bone and teeth separately  [18]. However, the latter study lacked a hold-out test dataset, included only scans devoid of metal artifacts and did not differentiate between maxillary and mandibular structures. Our results for the segmentation of mandibular canals (vDSC of 81. 59% ± 5. 79%, sDSC of 97. 9% ± To conclude, this study showed that nnU-Net could be used out-of-the-box (along with a simple postprocessing volume filter) to provide robust segmentation of routine preoperative CMF CT scans.
our database, patient mean age was 27 ± 11 years (minimum age 14, maximum age 66). The patients presented diverse anatomical deformities. 237subjects (52. 3%) exhibited skeletal class II (prognathic Some subjects had no upper teeth (n = 4), no lower teeth (n = 1) or no teeth at all (n = 2). The public mandible test dataset included senior patients (63 ± 9 years) with no teeth at all. Table2summarizes patient characteristics in our datasets.
Descriptive characteristics of the patients in the train/validation, test and public mandible test datasets. Prognathic maxilla and/or retrognathic mandible. b Retrognathic maxilla and/or prognathic mandible. c Clear asymmetry of the maxilla and/or the mandible evaluated on the 3D models. d Fixation implants from a previous surgery. e Skeletal classification cannot be provided due to missing teeth and lack of information about the mandible's vertical position during CT scan acquisition. f No information provided with the database. SD, Standard Deviation.
our database, patient mean age was 27 ± 11 years (minimum age 14, maximum age 66). The patients presented diverse anatomical deformities. 237subjects (52. 3%) exhibited skeletal class II (prognathic Some subjects had no upper teeth (n = 4), no lower teeth (n = 1) or no teeth at all (n = 2). The public mandible test dataset included senior patients (63 ± 9 years) with no teeth at all. Table2summarizes patient characteristics in our datasets. maxilla and/or retrognathic mandible), 163 subjects (36. 0%) exhibited skeletal class III (retrognathic maxilla and/or prognathic mandible) and 165 subjects (36. 4%) displayed asymmetry (clear asymmetry of the maxilla and/or the mandible evaluated on the 3D models). 89. 6% of the CT scans (n = 406) showed metallic artefacts, in the form of orthodontic materials for 354 subjects (77.
In future works we plan to fine-tune this model in order to evaluate its performance with CBCT scans, another widespread and challenging imaging modality. Finally, we will attempt to use our segmentation results for automatic localization of anatomic landmarks, in order to provide cephalometric measurements for clinical diagnosis and treatment planning[12,16,17,24]. 3. 51%) are significantly superior to those reported by Jaskari et al. (vDSC of 57%) and Kwak et al. (average Jaccard coefficient of canal and background of 57. 72%) [10, 11]. However, most existing studies used CBCTs, which are more difficult to segment than CT scans [16]. In addition, the absence of a public dataset of full high-resolution CMF (CB)CT scans with ground truth segmentations prevents direct comparison of our trained nnU-Net model with previously described ones.
This research has several limitations. It is a single-center study, and thus cannot assess the reliability of the results in other cohorts. Generalization of our results to other CT machines or deformities was partially evaluated on 10 public CT scans obtained using a different CT machine and presenting very different anatomies from those in our training dataset. No subjects from our training database had such edentulous mandibles or endotracheal tube. Setting aside mandible #10 on the basis of [6]otracheal tube interference, our vDSC results nonetheless outperformed semi-automatic open source segmentation algorithms tested on the same dataset[6]. This suggests that the trained nnU-.
Background: The toxicological risk of Co-Cr dental alloys is actually a sensitive subject with the European regulatory changes, namely regulation (EU) 2017/745 and annex VI to the CLP regulation (EC) 1972/2008. Objectives: The objective of this review is to conduct a rigorous analysis of the cytocompatibility of cobalt-chromium (Co-Cr) dental alloys. Considering various parameters such as cytotoxicity, type IV hypersensitivity reaction, sensitization, and irritation, we investigated evidence of toxicity of Co-Cr in human dental applications. Data sources: Specific search strategies were performed in three electronic databases, namely Medline, Embase, and Web of Science, using a main restriction in the search regarding the publication date. Study selection: Out of a total of 836 articles, only 21 studies were selected and analyzed according to PRISMA methodology.
Data Sources and Search Strategy This review was prepared according to the PRISMA statement  [6]. Three databases, namely PubMed, Embase, and Web of Science, were systematically screened. The references cited in the articles were also checked and could be included in the search. Search strategy was developed according to ISO 7405:2018 (evaluation of cytocompatibility of medical devices used in dentistry) and performed in these selected electronic databases. The search terms were divided in three parts: (i) CoCr alloy, for example, cobalt-chromium, Co-Cr, and CoCr; (ii) toxicity, for example, cytocompatibility, toxicity, cytotoxicity, sensitization, allergy, dermatitis, irritation, and genotoxicity; and (iii) dental fields, for example, dentistry, dental, prosthodontic, mucosa, periodontics, saliva, enamel, and oral.
Study Selection The titles and abstracts of all articles identified by the electronic search were read and assessed by two authors (A. V. and B. G. ) until March 2022. All titles and abstracts were examined and selected in accordance with the eligibility criteria. Those that appeared to fulfill the inclusion criteria or with insufficient data in the title and the abstract were selected for full analysis. The two authors were also the two reviewers and independently assessed the full-text articles. Any disagreement on the eligibility of studies included was resolved through discussion and consensus. One author (A. V. ) extracted the data using a pre-piloted data collection form, and a second author (B. G. ) verified data extraction independently for completeness and accuracy. Data obtained were as follows: general study details (first author, date of publication), type (in vitro/in vivo), population or sample size, criteria, methods, and outcomes of studies.
For example, Al-Imam et al. [2]  reached a different conclusion. Together with Co release from used functional and new non-functional prostheses, the authors investigated contact allergy to the alloys. Co release from prostheses was examined with the Co spot test and contact allergy by patch testing. In a 1-5-year study with 66 participants, none of them reported allergic reactions to Co. This study was conducted following previous findings that Co alloys were associated with elicit allergic reactions in Co-allergic patients. However, Al-Imam et al. [2]  provided contrary results suggesting that Co prostheses are safe to use in dentistry. In conclusion, there are no consistent data for Co-Cr-allergizing properties. Some findings clearly support Co-Cr biocompatibility without any allergic reactions though these are not unequivocal.
Study of the microanatomy of dental tissues The HELIX crown section was mounted on a glass slide for observation and analysis. The section was analyzed using incident light with a stereomicroscope Leica M8 and transmitted light with a Zeiss Universal photomicroscope. The Zeiss microscope was fitted with an Idea camera connected to a computer using Spot software (Version 5. 4). The images were processed with Nikon ViewN2 and their analysis was performed with ImageJ. Analysis of the dental microanatomy allows the study of the daily secretion rate (DSR) of the enamel and the formation time of the crown thanks to the presence of periodical growth lines in the enamel, the cross-striations, and the striae of Retzius (Figure  S1 ). Cross-striations reflect the circadian variation of the enamel secretion, their spacing is indicative of the amount of enamel formed per day and yields the DSR.
Raman microspectroscopy Raman analyses were performed using a Senterra Raman microspectrometer (Bruker Optics), using a laser emitting at 785 nm settled to provide a laser power at the sample of about 25 mW. Data collection was controlled by the OPUS 7. 5 software (Bruker Optics). Measurements were collected across one spectral window (450-1800 cm -1 ) at a spectral resolution of about 3 cm -1 , and each analysis was the coaddition of two spectra accumulated at up to 30 s of exposure time for each. Analyses were performed using a 50× objective (Olympus, Tokyo, Japan), giving an analytical spot size of approximately 12 µm in diameter, with the used excitation wavelength. Analyses were directly performed on polished surfaces of the thin sections. For mapping, the sample was moved by a computer-controlled stage.
), the second of which was collimated. Both detectors were positioned symmetrically at scattering angles of 135 degrees at a distance of 9 mm from the focal point at the sample surface. The use of dual-detector "backscatter" geometry maximizes the solid angle during the analysis of thin polished samples (∼110 µm-thick on average in this study), and allows large area to be scanned with micrometric resolution using millisecond dwell times. 46 This setup allowed capturing Kα emission lines from Si to Sr, with varying detection efficiency. Spectral peak deconvolution and integration were performed using the core of PyMca 5. 5. 0. 47 Image analysis was performed in HDIP v-1. 3. 3. 1073 (Teledyne CETAC Technologies, Bozeman, MT, USA). The X-ray yield calculations were performed using an in-house script assuming a hydroxyapatite matrix (Ca 10 (PO 4 ) 6 (OH) 2 ) with density 2.
ACKNOWLEDGMENTS The authors do thank  David Montero  from the  Institut des Matériaux de Paris Centre  ( IMPC FR2482 ) for servicing FEG-SEM & EDX instrumentation and  Sorbonne Université ,  CNRS  and  C'Nano  projects of the  Région Ile-de-France for funding (Paris, France). The authors also thank the  DESY User Office ,  Dr. Gerald Falkenberg , and those who have developed techniques employed in this study. This research was supported in part through Maxwell computational resources operated at  DESY. We are grateful to  Dr. Anastasia Brozou  for access to beamtime granted on her proposal I-20190203. This study received financial support from  Université Paris Cité , from the  T-JUST program  ( ANR-17CE14-0032-02 ), the  Max Planck Society , the  CNRS , and benefited from the scientific framework of the  University of Bordeaux 's  IdEx "Investments for the Future" program/GPR "Human Past.