dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding Open access funding provided by The  Science, Technology & Innovation Funding Authority (STDF)  in cooperation with The Egyptian Knowledge Bank (EKB). The present study is part of Egypt's national oral health survey (2013-2014) which was supported by a grant from the  World Health Organization , country office, Cairo, Egypt. (WHO-Reference: 201 313 i  9786-0 , Purchase order:  200769096 ). The funder provided support in survey implementation but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Literature Search A literature search was conducted by two independent authors (L. H. and R. B. ) up to July 15, 2021. Different electronic databases were selected: PubMed MedLine, Scopus, ISI Web of Science, Cochrane library, and EMBASE. These databases were used for identifying articles that could fit the specific criteria. The keywords, as well as the search strategy performed in PubMed, are summarized in Table  2. The search strategies for Scopus, ISI Web of Science, Cochrane library, and EMBASE databases are presented as Supplementary Materials (Tables  S1-S4 ). The investigators manually checked the list of references of each included paper for the discovery of additional manuscripts. After the search, the articles were introduced into Mendeley Desktop 1. 17. 11 software (Glyph & Cog, LLC, London, UK) to eliminate duplicates.
Study Selection Two researchers (L. H. and R. B. ) independently assessed the abstracts and titles of all the articles. Studies for full-text review were chosen based on the following eligibility criteria: (1) randomized controlled trial or in vitro papers studying the effect of using either digital shade selection or visual shade selection on the accuracy of color selection; (2) publishing in the English language; (3) assessing the accuracy of color measurement of the tooth and/or restorative material; (4) including a numerical value for the difference in color of the subject selected. Case series, reviews, case reports, and pilot studies were excluded. Full versions of any potentially suitable manuscripts were analyzed. Manuscripts that met the inclusion conditions or had inadequate data in the abstract and title to provide a clear judgment were considered for full evaluation.
Data Extraction The information of concern from the papers included was organized using a harmonized form in a Microsoft Office Excel 2019 spreadsheet (Microsoft Corporation, Redmond, WA, USA). These records comprised demographic data (author and year of publication), method used for shade match, materials used, type of room light, the main outcome, and main results. The corresponding authors of the included articles were communicated twice via e-mail to regain any missing data. If the investigators did not answer within 10 days of the first communication, the omitted data was not incorporated.
Statistical Analysis Meta-analyses were completed using the Review Manager Software version 5. 3. 5 (The Nordic Cochrane Centre, The Cochrane Collaboration, Copenhagen, Denmark). For the quantitative data, a random-effect model was conducted for evaluates, and pooledeffect estimates were acquired by comparing the standardized mean difference of the (∆E) between the classical shade guide and the computerized methods. Subgroup analyses were accomplished using a spectrophotometer or a digital camera to assess the shade selection. For the qualitative data, global analysis was conducted using a fixed-effects model, and pooled-effect estimates were acquired by comparing the risk difference of incorrect shade matching of the computerized methods (use of spectrophotometer or digital camera), or the conventional visual shade guide.
Results A total of 4136 papers were obtained from the databases (Figure  1 ). After removing the duplicates, the total quantity of literature found was 3299 publications for the primary examination. Then, 3268 manuscripts were excluded after revising the titles and summaries, leaving a total of 31 articles to be assessed for full-text review. Of these, 18 studies were excluded  [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33]. Exclusion reasons are assumed in the PRISMA flow diagram of the research, which resulted in a total of 13 manuscripts in the meta-analysis  [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46]. These manuscripts included seven in vitro studies and six clinical trials. The characteristics of the manuscripts incorporated in this systematic review were recapitulated in Table  3.
Glyceraldehyde 3-phosphate dehydrogenase (GAPDH; Mm99999915_g1) was used as a housekeeping gene to normalize the amount of mRNA present in each reaction. PCR was performed in 20 μl reaction mixtures containing the TaqMan Fast Advanced Master Mix, cDNA template (20 ng/well), primers, and probes using a QuantStudio 3 Real-Time PCR system (Thermo Fisher Scientific). The optimized thermal cycling conditions were as follows: 20 min at 95 °C, followed by 40 cycles per 1 min at 95 °C, and 20 min at 60 °C. To compare the expression levels among different samples, the relative expression level of the genes was calculated by the comparative CT (ΔΔCT) method using QuantStudioTM Design & Analysis Software.
Metagenome shotgun sequencing and microbiome data production and analyses Shotgun metagenomic sequencing library preparation was performed by Novogen, Inc. The libraries were prepared according to a standard protocol from Illumina, and at least 1 Gb of 150 bp pair-end reads per sample were sequenced on the Illumina Hiseq4000 machines. FASTQ files were generated from the sequencing machines and used for the analyses of the bacteriome/microbiome and virome as described below.
Statistical analysis SPSS 21. 0 statistical software (IBM, Chicago, IL, USA) was used for statistical analysis of the non-sequencing data. Student's t-test was used to compare two independent groups. For comparison of intrabony defects, data were expressed as frequency and percentage, and a chi-square test was used for analysis. Further, analyses of the PCR data from the oral swabs and quantification of inflammatory cells were performed using an ANOVA followed by a Tukey's test. Data were presented as means ± standard deviations (SD). Values of p < 0. 05 were considered significant. For the microbiome/virome analyses, we normalized the data to have 1 million reads per sample (reads per million, RPM). We filtered out taxa with average read counts less than 1 RPM per standard protocols. We removed five samples, namely Infection 1, Infection 6, Non-nisin L.
Simple Summary: New personalized cancer prevention strategies may decrease the mortality of oral cancer that can arise from oral potentially malignant disorders (OPMD). A major cancer hallmark is the acquisition of multiple deletions or amplifications of genomic material fragments leading to genomic instability (GI). Our goal was to identify a set of genes whose expression was associated with GI. A total of 20 genes correlated with GI were identified in two independent datasets of head and neck cancer (including oral cancer). We computed a score of those genes, referred to as the GIN score, in with each sample from multiple validation datasets. We show that the GIN score: (i) was correlated with GI, (ii) increased at different stages of oral carcinogenesis from normal mucosa to oral cancer, and (iii) was associated with malignant transformation of OPMD.
Previous studies identified gene-expressionbased signatures of genomic instability in different cancer types that were associated with patients outcome  [17] [18] [19] [20]. We hypothesized that a gene-expression-based surrogate of genomic instability in HNSCC may help in identifying OPMD with a high risk of malignant transformation. Using 520 HNSCC from The Cancer Genome Atlas (TCGA) together with 32 HNSCC cancer cell lines from the Cancer Cell Line Encyclopedia (CCLE), we identified 20 genes correlated to GI, as defined by the fraction of genome altered (FGA). An enrichment score of this gene set, referred to as the genomic instability (GIN) score, was then computed in multiple datasets. The GIN score was confirmed to be associated with FGA in two independent validation datasets of squamous cell carcinoma of the esophagus and lung from TCGA.
The Cancer Genome Atlas (TCGA) We queried the TCGA database in order to retrieve the clinical and gene-expression profiles of 520 HNSCC (HNSC-TCGA), including 421 and 97 HPV-negative and -positive tumors, respectively. Clinical data and normalized read counts generated from RNAsequencing were downloaded using the TCGA2STAT R-package  [21]. A similar approach was used to retrieve data from 501 lung squamous cell carcinomas (LUSC-TCGA) and 95 esophagus squamous cell carcinomas (ESCC-TCGA) from TCGA. Clinical data were downloaded from the cBioPortal database  [22, 23].
Gene Expression Omnibus The expression profiles of 252 and 138 primary HNSCC were retrieved from the Gene Expression Omnibus database: GSE65858  [24]  and GSE39366  [25] , respectively. Molecular subtypes, as previously described  [11, 25] , HPV status, and clinical information were available for tumors included in GSE39366 and GSE65858. Cancers 2022, 14, 834 3 of 13.
Datasets including OPMD or Oral Epithelial Dysplasia (OED) Six independent gene-expression datasets including oral leukoplakia, the most common OPMD, or oral epithelial dysplasia (OED), as defined according to recent papers on the nomenclature and terminology of oral premalignant conditions  [2, 26] , were downloaded from Gene Expression Omnibus and are referred to as OPMD and OED datasets. Clinical data and gene-expression profiles were retrieved in both datasets. The OPMD-1 dataset included 1 normal oral tissue, 15 OPMD, and 34 OSCC (GSE85195)  [27]. Moreover, copy number profiles were retrieved for 10/15 OPMD and 23/34 OSCC from this dataset. Raw CGH data (Agilent Human Genotypic Designed CGH 105k Microarray) were downloaded from GEO (GSE85514) in order to perform normalization and segmentation using the rCGH R package  [28].
RNA sequencing gene-expression profiles of 20 oral leukoplakia (OL) patients with available information regarding malignant transformation during follow-up were retrieved from GSE156206 in order to be included in the OPMD-2 dataset. During the follow-up, 10/20 OL underwent malignant transformation. The median time to malignant transformation was 17 months (IQR: 42. 75 months). A third OPMD dataset set, OPMD-3, included whole-genome gene-expression profiles (GSE26549) of 86 OL biopsies in patients followed prospectively in a chemoprevention trial with a median follow-up of 7 years (95% CI  (5. 6-8. 6) ). In this trial, patients with OPMD were randomly assigned to treatment with 13-cisretinoic acid (13cRA) versus retinyl palmitate (RP) with or without b-carotene (BC). In 70/86 OL, we retrieved loss of heterozygosity status at different microsatellite markers 9p21 (D9S171, D9S1747), 3p14 (D3S1285), 17p13 (D17S1176), TP53, and 8p22 (D8S254)  [29].
The OED-1 dataset included 45 normal oral tissue, 17 oral dysplasia, and 167 oral SCC (GSE30784)  [30]. The OED-2 dataset included 30 paired normal oral tissue, dysplasia, and carcinomas (CIS or SCC) from 10 patients, with available normalized gene-expression profiles downloaded from GSE46802  [31]. The OED-3 dataset also included 33 paired oral normal, dysplasia, and SCC from 11 patients with available normalized gene-expression profiles downloaded from GSE35261  [32]. A detailed description of the different datasets, including data normalization and processing, is provided in Supplementary Table  S1.
Surrogates of Genomic Instability The mutational load and fraction of genome altered (FGA), which is defined as the length of segments with log 2 or linear CNA value larger than 0. 2 divided by the length of all measured segments, were retrieved for TCGA and CCLE samples from the cBioportal database  [22, 23]. The FGA was also computed in OPMD and OSCC samples from GSE85515 using the CINmetrics R package  [33]. In order to identify a gene-expression-based surrogate of genomic instability, we selected specific genes whose expression was consistently correlated associated with FGA, using a similar approach as previously described  [20]. Single-sample Gene Set Enrichment Analysis tool (ssGSEA)  [34, 35]  was run using the GSVA r package  [36]  in order to compute the enrichment score (ES) of those selected genes, namely, the genomic instability (GIN) score.
Bioinformatics and Statistics Bioinformatics and statistics were performed using Array Studio software (Omicsoft Corporation), and Bioconductor packages in the R language  [19]  and GraphPad Prism version 6. 00 (San Diego, SA, USA). Normalization and processing of copy-number and gene-expression data are detailed for each dataset in Supplementary Table  S1. Unpaired Mann-Whitney and Kruskal-Wallis tests were performed to compare continuous values in two or more than two groups, respectively. Paired Mann-Whitney and Friedman tests were used to compare continuous values from paired samples in two or more than two groups, respectively. The Pearson correlation coefficient (r) was estimated to measure the strength of a linear association between two continuous variables. Progression-free survival (PFS) was defined by time in months: from tumor biopsy to death, recurrence, or loss to follow-up (GSE39366); from tumor biopsy to a new tumor event (TCGA); from the registration date to the detection of either progression (as defined as local recurrence, new lymph node or distant metastasis, or second primary carcinoma), or death (GSE65858).
In the 86 samples from the OPMD-3 dataset (GSE26549), oral cancer-free survival (OCFS) was defined as the time from the first biopsy to oral cancer or to the date of last follow-up (for censored patients). First, the association between OS, PFS, and OCFS, and the GIN score was tested using a univariate cox model. Then, the distribution of OCFS was estimated using the Kaplan-Meier method and compared with the log-rank test between groups of patients defined by the level of genomic instability (high GIN vs low GIN) according to the GIN score. The cutoff value for the GIN score to group patients into high and low GIN was determined using the Maxstat R package to identify the value that correspond to the most significant relation with OCFS. A multivariate Cox proportional hazard model, including GIN group, treatment arm (beta-carotene, 13-cis-retinoic acid or retinyl palmitate), and histological grade (hyperplasia or dysplasia) was also built to test the association of the GIN group with oral-cancer-free survival in patients with OPMD from the OPMD-3 dataset, after testing for proportional hazard assumptions.
Identification and Validation of Gene-Expression-Based Genomic Instability (GIN) Score Using a similar approach as previously described  [20] , we tested the correlation between whole-genome expression and the fraction of genome altered (FGA), a measure of genomic instability, in 520 HNSCC from TCGA and in 32 cancer cell lines of the upper aerodigestive tract from CCLE. All genes were ranked according to Pearson's coefficient of correlation in the TCGA-HNSC and CCLE datasets. Different thresholds of correlation (≥0. 2, ≥0. 3, ≥0. 35, ≥0. 4) were tested in order to select a relevant number of genes (~10 to 50) to be included into a gene signature of genomic instability (Supplementary Table  S2 ). Using a threshold of 0. 35, a total of 20 overlapping genes between these two datasets was identified (Figure  1A ) and selected for further analysis (Table  1 ).
There were no overlapping genes between those genes and the CIN70 signature that had been established and validated as a surrogate of chromosomal instability across different cancer types. Pathway enrichment of this set of 20 genes was analyzed using the EnrichR tool  [37] [38] [39]. The two most enriched terms from the Reactome 2016 library were 'chromatin organization Homo sapiens R-HSA-4839726 and 'chromatin modifying enzymes Homo sapiens R-HSA-3247509 (Q-value = 0. 0443), related to 3 genes (MTA1, SMYD3, CDK4) from our gene set. Using the ssGSEA tool, we computed the enrichment score of those 20 genes, referred to as the GIN score, in the 520 HNSCC sample from TCGA-HNSC, and in the 501 lung squamous cell carcinoma (LUSC) and 95 esophagus squamous cell carcinoma (ESCC) from the TCGA-LUSC and TCGA-ESCC datasets, respectively.
In order to validate this score as a surrogate of genomic instability, we tested the correlation between GIN score and FGA in both datasets. Significant correlation was found between FGA and GIN score in TCGA-HNSC (r = 0. 65, p < 0. 0001, Figure  1B ), and in TCGA-ESCC (Figure  1C , r = 0. 59, p < 0. 0001) and TCGA-LUSC (r = 0. 63, p < 0. 0001, Figure  1D ). Conversely, the GIN score was not consistently correlated with the mutation count in samples from TCGA-HNSC (r = 0. 19, p = 0. 0012), TCGA-LUSC (r = 0. 01, p = 0. 8958), and TCGA-ESCC (r = -0. 09, p = 0. 4018). Lastly, GIN score and FGA were computed in 10 OPMD and 23 OSCC from the OPMD-1 dataset. Significant positive correlation was also found between GIN score and FGA during oral carcinogenesis (r = 0. 38, p = 0. 0292).
GIN Score Is Associated with the Molecular Classification of HNSCC In large genomic profiling studies of HNSCC, four distinct molecular subtypes are consistently reported: atypical, basal, classical, and mesenchymal  [11, 25]. The classical subtype of HNSCC is recognized to harbor a high level of genomic alterations  [14, 15]  compared to others. The GIN score was computed using the ssGSEA tool in 520, 138 and 253 HNSCC from TCGA-HNSC, GSE39366, and GSE65858, respectively. The GIN score was statistically different across molecular subtypes in both datasets (p < 0. 0001), and consistently higher in classical compared to atypical, basal, and mesenchymal HNSCC (Figure  2A-C ). No significant difference was found HPV-negative and -positive HNSCC in both datasets (Supplementary Figure  S1 ). We tested the association of the GIN score with the survival of patients with HNSCC from TCGA-HNSC, GSE39366, and GSE65858.
No significant association was found between GIN score and survival using a univariate Cox model in patients from TCGA (OS: p = 0. 078; PFS: p = 0. 463), GSE39366 (PFS: p = 0. 493) and GSE65858 (OS: p = 0. 393; PFS: p = 0. 398), and using Kaplan-Meier curves and a log-rank test (Supplementary Figures  S2-S4 ).
GIN Score Increased from Dysplasia to OSCC during Oral Carcinogenesis In order to assess the dynamics of the GIN score at different stages of oral carcinogenesis, we computed the GIN score in five independent datasets: (i) oral normal tissue, (ii) oral epithelial dysplasia (OED) or oral potentially malignant disorders (OPMD), and (iii) oral carcinomas. First, the GIN score was computed in the OPMD-1 dataset including 1 normal oral mucosa, 15 oral leukoplakia (OL), and 34 OSCC (Figure  3A ), and was higher in OSCC compared to OPMD (Mann-Whitney test, p = 0. 0016). We tested the association of the GIN score with the survival of patients with HNSCC from TCGA-HNSC, GSE39366, and GSE65858. No significant association was found between GIN score and survival using a univariate Cox model in patients from TCGA (OS: p = 0.
GIN Score Increased from Dysplasia to OSCC during Oral Carcinogenesis In order to assess the dynamics of the GIN score at different stages of oral carcinogenesis, we computed the GIN score in five independent datasets: (i) oral normal tissue, (ii) oral epithelial dysplasia (OED) or oral potentially malignant disorders (OPMD), and (iii) oral carcinomas. First, the GIN score was computed in the OPMD-1 dataset including 1 normal oral mucosa, 15 oral leukoplakia (OL), and 34 OSCC (Figure  3A ), and was higher in OSCC compared to OPMD (Mann-Whitney test, p = 0. 0016). Moreover, in the OED-1 dataset including 45 normal tissue, 17 oral dysplasia, and 167 OSCC, the GIN score was also significantly increased from normal to dysplasia and OSCC (Figure  3B , Kruskal-Wallis Test, p < 0. 0001). The GIN score was then computed in 30 paired oral normal mucosa, epithelial dysplasia, and CIS/OSCC from 10 patients included in the OED-2 dataset (Figure  3C ).
The GIN score was statistically different among normal, dysplasia, and SCC samples (Friedman test, p = 0. 0303). The GIN score was increased from dysplasia to OSCC in 8/10 patients, but did not reach statistical significance (Mann-Whitney test, p = 0. 4316). In an independent dataset (OED-3) composed of 33 paired oral normal mucosa, epithelial dyspla- Moreover, in the OED-1 dataset including 45 normal tissue, oral dysplasia, and 167 OSCC, the GIN score was also significantly increased from normal to dysplasia and OSCC (Figure  3B , Kruskal-Wallis Test, p < 0. 0001). The GIN score was then computed in 30 paired oral normal mucosa, epithelial dysplasia, and CIS/OSCC from 10 patients included in the OED-2 dataset (Figure  3C ). The GIN score was statistically different among normal, dysplasia, and SCC samples (Friedman test, p = 0.
0303). The GIN score was increased from dysplasia to OSCC in 8/10 patients, but did not reach statistical significance (Mann-Whitney test, p = 0. 4316). In an independent dataset (OED-3) composed of 33 paired oral normal mucosa, epithelial dysplasia, and OSCC from 11 patients (Figure  3D ), the GIN score was also statistically different among normal, dysplasia, and SCC samples (Friedman test, p = 0. 0273), with a significant increase from dysplasia to OSCC (Mann-Whitney test, p = 0. 0098). Lastly, in the OPMD-3 dataset, we found an increased GIN score from OPMD with no histological change to OPMD with hyperplasia to OPMD with mild, moderate, or severe dysplasia (p = 0. 0276, Supplementary Figure  S5 ). Overall, the GIN score was statistically different between samples at different histological steps of oral carcinogenesis.
GIN Score Is Associated with Oral-Cancer Development in Patients with Oral Leukoplakia Because our data suggest that the GIN score could be associated with oral-cancer development, we computed the GIN score in 10 oral leukoplakia that had transformed into OSCC during follow-up and 10 oral leukoplakia without malignant transformation (OPMD-2 dataset). The score was significantly higher in malignant transforming OPMD (M-OPMD) compared to that in nonmalignant transforming (NM-OPMD) (Figure  4A , Mann-Whitney test, p = 0. 0288). The score was also computed in 86 OPMD from the OPMD-3 dataset in order to evaluate the association of the GIN score with oral-cancer-free survival (OCFS) using Kaplan-Meier curves (Figure  4B ). Using a univariate Cox model, an increased GIN score was associated with improved OCFS (p = 0.
0118). The enrichment score of the pan-cancer chromosomal instability signature (CIN70), as previously described, was also computed in the 86 OL. No significant association was found between CIN 70 score and OCFS in this dataset's (p = 0. 25) univariate Cox model. Then, in order to group patients into low and high levels of genomic instability (GI) according to GIN score, we used the Maxstat R package  [40]  in order to identify the optimal GIN score threshold (score = 82. 88) that corresponded to the most significant relation with oral-cancer-free survival. Patients suffering from OPMD with a high GIN score (>threshold = 82. 88) had shorter oral-cancerfree survival compared to OPMD with a low GIN score (p = 0. 0081). Using a multivariate Cox model including histological grade (hyperplasia vs dysplasia) and treatment arm, patients suffering from OPMD with a high GIN score had shorter oral-cancer-free survival (HR = 3.