dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods We searched the Medline ® database for articles addressing drug-induced hyperglycemia and diabetes up to January 31, 2023. Relevant articles were selected by reading the abstract if they can identify drugs associated with altered glycemic levels, with the exception of antidiabetic agents. We also analyzed the publications cited by the selected articles to identify those that would not have been initially retrieved. The selection of at-risk drugs was also supplemented by including the drugs associated with hyperglycemia and diabetes according to official data from the French and international drug information databases Thériaque and Micromedex. We present the main drugs and drug classes likely to induce hyperglycemia or diabetes, classified by major organ system. The aspects relating to the mechanisms of action potentially involved completes the presentation of each drug/class.
For each drug/class involved, a disproportionality analysis with the case/non-case method was carried out using pharmacovigilance data from the international database VigiBase ®. VigiBase ® is the world's largest pharmacovigi-Therapie 79 (2024)  [221] [222] [223] [224] [225] [226] [227] [228] [229] [230] [231] [232] [233] [234] [235] [236] [237] [238]  lance database, involving over 130 national pharmacovigilance centers and including over 35 million individual case reports (ICSRs)  [5]. Case/non-case analysis allows the detection of a pharmacovigilance signal for a drug/adverse reaction pair, showing a higher than expected number of reported cases  [6]. Cases of drug-induced hyperglycemia were defined by the high level terms (HLT) ''hyperglycemic conditions'' or ''diabetes mellitus'' of the Medical Dictionary for Regulatory Activities (MedDRA) classification  [7]  and non-cases were all other reported adverse reactions.
Antiretrovirals Several studies show that the risk of hyperglycemic disorders and diabetes is strongly associated with antiretroviral therapy used to treat human immunodeficiency virus (HIV) infection, in particular nucleoside reverse transcriptase inhibitors (NRTI) and protease inhibitors (PI)  [99] [100] [101] [102] [103]. In fact, antiretrovirals were the drug class with highest number of reports of hyperglycemia in the French pharmacovigilance database in a study in 2008  [104]. However, antiretrovirals that are more recent appear to have fewer metabolic adverse drug reactions. First-generation NRTIs (stavudine and zidovudine) are associated with adipose tissue macrophage infiltration and inflammation, mitochondrial toxicity and adipose cell dysfunction including adipocyte insulin resistance  [105].
Tyrosine kinase inhibitors Several tyrosine kinase inhibitors (TKIs) such as alectinib, axitinib, ceritinib, dabrafenib, rociletinib and trametinib have been reported to cause hyperglycemia  [124]. Indeed, the official information for ceritinib, dabrafenib and trametinib includes specific warnings and precautions in this regard. An analysis of the Japanese pharmacovigilance database suggested an excess risk of hyperglycemia with ceritinib  [125]. Understanding the molecular mechanisms involved is complicated by the fact that TKIs of the same therapeutic or chemical class may be associated with either hypoglycaemia or hyperglycaemia  [124]. A probable hypothesis is that the structures of the adenosine triphosphate-binding sites of TKIs are similar to those of the insulin receptor, leading to its blockade  [125].
Disproportionality analyses for at-risk drugs Disproportionality analyses based on pharmacovigilance data from the international database VigiBase ® concerning cases of diabetes or hyperglycemia reported with drugs identified as at risk in the literature or official documents are presented in Table  1. We observe that a statistically significant pharmacovigilance signal is present for a majority of drugs or pharmacological classes identified as being at risk of diabetes or hyperglycemia. Some drugs stand out with a high ROR: HMG-CoA reductase inhibitors (ROR 8. 7), diazoxide  (ROR 11. 64)  It is logical to retrieve a pharmacovigilance signal for drugs otherwise identified as being at risk of diabetes or hyperglycemia, this signal being potentially amplified by a notoriety effect  [232]. On the other hand, drugs described as being at risk and for which no pharmacovigilance signal was found can be considered false negatives (e.
g. clonidine, anti-arrhythmics, antidepressants, phenytoin, isoniazid, rifampicin, nonnucleoside reverse transcriptase inhibitors, chemotherapies, adalimumab, NSAIDS, sex hormones). There are several possible causes for false positives: (i) the effect can be real but not strong enough, too rare or occurring in specific population (e. g. pediatrics) or conditions (e. g. overdose); (ii) the effect only concerns one or few drugs in the class (e. g. gatifloxacin); (iii) the effect can suffer from a lower level of awareness with greater under-notification  [233] , or (iv) the signal can be biaised (under-estimated) by competition bias  [234]  with drugs known to be associated with hyperglycemia risk, especially as the disproportionality analyses were carried out on the entire database and not according to indication or therapeutic area.
In France, mushroom picking is part of the culture. The practice is not without risk, as around 1300 people are poisoned each year by eating wild mushrooms on the French mainland. However, this practice is not part of local culture on Reunion Island, a French territory located in the Indian Ocean. Indeed, there are practically no reports of mushroom poisoning on Reunion Island. Here we describe, through a retrospective study, cases of mushroom poisoning recorded in the Indian Ocean toxicosurveillance system database from 2020 to 2021. Overall, 19 people were poisoned following wild mushroom ingestion on Reunion Island: 15 in 2020 and 4 in 2021. Six events were recorded: the majority of poisonings involved clusters (n = 5) during a shared meal (pan-fried), only one case was single mushroom poisoning by ingesting a raw mushroom.
Study justification To date, descriptions of mushroom poisoning on Reunion Island has been almost non-existent: only one scientific publication dating from 2016, of a single case of mushroom poisoning exists  (Schmitt et al. , 2016). This case report describes the case of a 43-year-old patient who developed a gastrointestinal irritant syndrome, which includes abdominal pain, profuse diarrhea, nausea and vomiting, 1h30 after eating 3 specimens of cooked Chlorophyllum molybdites. He harvested the mushroom himself in the south-west of the island, but thought he had been picking Volvariella sp. The aim of this study is to describe cases of intoxication following mushroom consumption recorded in the database of the Indian Ocean toxicosurveillance system from 2020 to 2021. This retrospective study is the first to provide a description of this type of poisoning in this overseas territory, serving as a reference for health monitoring and preventive actions.
Materials and methods This is a retrospective descriptive study of cases of accidental poisoning by wild mushroom ingestion between 2020 and 2021, from the Indian Ocean toxicosurveillance system database. The Indian Ocean Toxicovigilance Department is in charge of monitoring human poisoning on Reunion Island. Since 2020, the database has been updated to be in line with the national database of poison control centers, which explains the recruitment period. The data in the Indian Ocean Toxicovigilance Department's database mainly comes from consultations at the 4 public emergency departments. The cases investigated are based on a weekly extraction of codings for main diagnoses related to poisonings from emergency room visits. A case is defined as a person who has consumed one or more macromycetes (wild mushroom) and has presented at least one clinical sign following this ingestion.
Discussion Although the custom of mushroom picking is much less prevalent on Reunion Island than in mainland France, we have noticed that there are regular cases of wild mushroom poisoning. In our study, we assessed 19 cases of mushroom poisonings for the years 2020 and 2021 distributed over five clusters and one isolated case. In mainland France around 1300 wild mushroom poisoning cases are reported each year according to data from poison control centers  (Sinno-Tellier et al. , 2019). In our study, mushrooms were eaten after being pan-fried and were usually shared with family members and/or close friends. These consumption habits are typically also found in mainland France (Sinno-- shared dish account for half of all poisoning cases in France. Nevertheless, it is difficult to make comparisons given the small number of cases we were able to identify.
27 fter each visit, the bases were digitized by the same investigator (Y. I. ). A power analysis was conducted, and a sample of 18 was determined as sufficient to detect significant differences in retention between the tested denture bases. A total of 20 participants were recruited to compensate for possible dropouts during the study. 28 A statistical software program (IBM SPSS Statistics, v24; IBM Corp) was used for all statistical analyses. Kolmogorov-Smirnov tests were used to assess the normality distribution of the continuous variable "retention" in each group. Wilcoxon tests were applied to compare the retention forces between the conventional workflow (MB1, PB1) and the digital workflow (MB2, PB2). The correlation between denture base fit and the corresponding retention forces was investigated using the Pearson correlation coefficient; preliminary analyses were performed to ensure no violation of the assumptions of normality, linearity, and homoscedasticity.
Introduction: Skeletal stability after bimaxillary surgical correction of Class III malocclusion was investigated through a qualitative and quantitative analysis of the maxilla and the distal and proximal mandibular segments using a 3-dimensional voxel-based superimposition among virtual surgical predictions performed by the orthodontist in close communication with the maxillofacial surgeon and 12-18 months postoperative outcomes. Methods: A comprehensive secondary data analysis was conducted on deidentified preoperative (1 month before surgery [T1]) and 12-18 months postoperative (midterm [T2]) cone-beam computed tomography scans, along with virtual surgical planning (VSP) data obtained by Dolphin Imaging software. The sample for the study consisted of 17 patients (mean age, 24. 8 6 3. 5 years).
Using 3D Slicer software, automated tools based on deep-learning approaches were used for cone-beam computed tomography orientation, registration, bone segmentation, and landmark identification. Colormaps were generated for qualitative analysis, whereas linear and angular differences between the planned (T1-VSP) and observed (T1-T2) outcomes were calculated for quantitative assessments. Statistical analysis was conducted with a significance level of a 5 0. 05. Results: The midterm surgical outcomes revealed a slight but significantly less maxillary advancement compared with the planned position (mean difference, 1. 84 6 1. 50 mm; P 5 0. 004). The repositioning of the mandibular distal segment was stable, with insignificant differences in linear (T1-VSP, 1. 01 6 3. 66 mm; T1-T2, 0. 32 6 4. 17 mm) and angular (T1-VSP, 1.
MATERIAL AND METHODS A secondary analysis of preexisting data prospectively collected for clinical purposes was performed. The medical protocol and ethics followed the Declaration of Helsinki. The Institutional Review Board approved the study (HUM00224130 -reference for the University of Michigan, Ann Arbor, Mich). To use radiologic data for scientific analysis, a specific informed consent form was signed by all patients. Deidentified CBCT scans (field of view of 17 3 20 cm; 400 mm; 110 kVp; 3. 175 s; 59 mSV) from 17 nongrowing patients (9 females and 8 males), aged 18-32 years (mean age, 24. 8 6 3. 5 years), with a Class III dentoskeletal malocclusion (A-point-Nasion-B-point angle \0 ; unilateral or bilateral Angle Class III molar relationship), who underwent bimaxillary orthognathic surgery with traditional approach and preoperative VSP, were included.
The VSP was conducted by an orthodontist in close communication with the surgeon using Dolphin Imaging software (Dolphin Imaging and Management Solutions, Chatsworth, Calif). After acquiring the presurgical CBCT scans, clinical records, and photographs, the orthodontist and maxillofacial surgeon discussed the treatment goals, including surgical, esthetic, and occlusal corrections. The orthodontist then conducted the VSP. This plan was presented to the surgeon for approval, with revisions made if necessary to align orthodontic and surgical objectives. Orthognathic surgery was performed using computeraided design and manufacturing intraoperative splints for jaw repositioning. The CBCT scans were collected up to May 2023 and stored in the Dropbox folder at the laboratory-protected server at the University of Michigan, School of Dentistry.
Patients with 1-jaw surgery, surgery-first approach, history of mandibular trauma, craniofacial syndromic malformations, systemic diseases, or osteometabolic disorders were excluded. Preoperative (T1) CBCT scans, performed 1 month before surgery, and postoperative (T2) CBCT scans taken at the midterm period, 12-18 months after surgery, were analyzed and compared with the VSP. The virtual simulation was performed by a trained orthodontist according to the objectives discussed with the maxillofacial surgeon to determine the jaw movements in all spatial directions (Fig  1 ). The resulting data were then exported as stereolithography files. Three-dimensional image analysis was conducted using the software 3D Slicer, using dedicated tools on the basis of recently validated deep-learning approaches.
[26] [27] [28]  A standardized orientation of all T1 CBCTs was performed according to the Frankfurt plane and the midsagittal plane. 28, 29  Automated voxel-based registration of postoperative CBCTs on oriented T1 scans was performed for each patient, selecting the cranial base as the reference area of superimposition because it is not modified by the surgery. 28  Automated bone segmentation allowed us to obtain the virtual models of each skeletal segment (cranial base, maxilla, and mandible). Quality control of the segmentations was performed using the ITK-SNAP software (version 3. 8. 0; http://www. itksnap. org). 26  Automated landmarks identification was performed; landmark position was then checked by an experienced orthodontist (Table  I ). 27  For the qualitative analysis, automated surface registration was performed to superimpose the skeletal models of VSP and T2.
30, 32  Considering the multiple factors involved in maxillary repositioning, the virtual planned movement of the maxilla could benefit from a slight sagittal overcorrection, particularly if the esthetic goals of orthognathic surgery include better dental exposure during smiling and improved support for soft tissues. The involvement of an experienced orthodontist using the Dolphin Imaging VSP module in this study provided valuable insights and feedback to the surgeon, benefiting from the orthodontist's expertise in postsurgical orthodontic treatment and contributing to optimizing the overall planning of skeletal modifications. Although the pursuit of esthetic goals remains paramount, a robust plan should also consider potential surgical intricacies that could hinder optimal jaw movements. In addition, the prediction of postsurgical occlusal relationships should be considered to facilitate subsequent orthodontic finalization.
The duration in situ for each RBFDP was recorded. A clinical example of an RBFDP at T0 and T1 is illustrated in Figure  1. The evaluations were transferred anonymously from the patient's record to a dataset with an electronic data capture software program (Research Electronic Data Capture; version 1. 2; Vanderbilt University). The exact number of days between T0 and T1 was calculated with a spreadsheet (Excel; version 14. 7. 3; Microsoft Corp) to determine the number of days at risk. To evaluate the association between the scores of the modified USPHS assessment criteria and the occurrence of changes in RBFDPs, the incidence density per criterion was determined over the total population and for each group (MH, BT, DPP). The difference in incidence density among the groups was then calculated.
The base shape was designed on the STL file of the scanned reference cast using a software program (AvaDent; Global Dental Science Europe BV), and 2 denture bases were manufactured from PMMA resin, 1 printed (PB1) (NextDent; Vertex-Dental BV) and 1 milled (MB1) (AvaDent Denture Base Puck; Global Dental Science Europe BV). The prostheses were finished and polished according to the standard technique used by each manufacturer. Additionally, another impression technique was evaluated; the corresponding results are reported separately in Part II  25  (Fig. 1 ). A visual analog scale (VAS) questionnaire was created inquiring about taste, smell, fit sensation of the base, pain on insertion and removal, and the smoothness of the bases (Appendix 1, available online). Participants were trained to use this type of questionnaire by means of an exercise sheet.
Participants were asked to complete an identical second questionnaire. All bases were scanned for a second time, and the files were imported into a comparison software program (Geomagic Control X 2020; 3D systems) for analysis of fit; deviations from the reference were reported in mm. The primary outcome being retention force, a power analysis was conducted based on the retention comparison between milled and conventional bases. 18 The difference in retention was used to determine an effect size (E) for the present study of 19. 910 with a standard deviation of the change in outcome S(D) of 29. 960. According to this analysis, a sample size of 18 would detect a significant difference (Type I error=0. 05, power test 1 e b=. 8, two-sided test). To account for dropouts, a total of 20 participants were recruited.
Statistical analyses were performed with a statistical software program (IBM SPSS Statistics, v24. 0; IBM Corp). KolmogoroveSmirnov tests were used to assess the normality distribution of the continuous variables "fit" and "retention" in each group. Friedman tests were used to compare the fit values and peak retention forces among conventional, printed, and milled bases in each zone. For each patient, the mean peak retention force was calculated for the overall 3 areas and was also analyzed statistically as overall mean peak retention force. Wilcoxon tests were used to compare the fit and retention forces, as well as VAS scores before and after storage in saliva for 2 weeks (a=. 05).
Statistical methods All summaries and statistical analyses were generated using SAS® version 9. 4. The levels of skin damage were analyzed for the three tested sunscreen formulae and compared to those of non-protected tissues exposed to the same UV-SSR doses. Unless otherwise specified, three skin samples of each of the three T-Skin™ batches were evaluated for each condition (i. e. , nine observations for each condition). Only two skin samples were evaluated for the analysis of epidermal and dermal viability (MTT assay) and DNA damage (CPD immunolabeling) giving six observations per condition. Qualitative variables were described as number and percent of the different response modalities. The 95% confidence intervals (CI) were calculated if necessary. The number of missing data was also given.
However, despite the recent progress, it is still difficult to provide a biological interpretation to an observation of change in specific sub-cellular or cellular parameters. This is particularly true for metabolomics, for which, a large proportion of the metabolites observed cannot be identified with the current information available in databases. In mass spectrometry, especially with liquid chromatography-mass spectrometry (LC-MS) analyses, only 4%-5% of the peaks can be identified in biological samples  [13]. This gap in the reference databases is also encountered in genomics  [14]  and proteomics  [15]. However, even if a biomarker cannot be characterized, it is still possible to model its response as a function of the dose of exposure. These dose-response curves are usually drawn following exposure to increasing concentrations of a substance in order to characterize its toxicity  [16].
Data compilation The process for selecting studies and selected extracted data observations (identification, screening, eligibility and inclusion in the meta-analysis) is reported in a flow diagram (Fig. S1 ). The global build-up database of the meta-analysis is available in the supplementary information section (Table  S1 ). On 4,126 identified articles, a total of 156 articles were selected for this metaanalysis and 3,176 observations of variation of biomarkers could be extracted. Among them, 581 categorized as "constant" or "N. D. " were subsequently removed. One hundred and nineteen species are represented along 18 phyla (Annelida: 552 observations, Arthropoda: 191, Ascomycota: 6, Cercozoa: 4, Charophyta: 32, Chlorophyta: 332, Chordata: 283, Ciliophora: 16, Cnidaria: 7, Cyanobacteria: 51, Echinodermata: 4, Euglenozoa: 3, Haptophyta: 24, Mollusca: 278, Myzozoa: 6, Ochrophyta: 97, Porifera: 6, Magnoliophyta: 688) and communities (biofilm: 15).
Response trends of biomarkers according to their category: defense or damage In the compiled database, biomarkers distribute almost equally between defense biomarkers (1,325) and damage biomarkers (1,270) (Table  S1 ). Among the defense biomarkers, 402 (30. 3%) had a bell-shaped trend, 299 (22. 6%) had a decreasing trend, 529 (39. 9%) had an increasing trend and 95 (7. 2%) had a U-shaped trend. This distribution was different for damage biomarkers, i. e. , 139 (10. 9%) had a bell-shaped trend, 439 (34. 6%) had a decreasing trend, 644 (50. 7%) had an increasing trend and 48 (3. 8%) had a U-shaped trend (Fig. 1A  and Table  2 ). The proportion of biomarkers with a biphasic trend (bell-shaped or U-shaped) is significantly higher for defense biomarkers than for damage biomarkers (Table  2 ) (p-value < 2e-16) whereas the proportion of biomarkers with a monotonic trend (decreasing or increasing) is significantly greater for damage biomarkers than for defense biomarkers (Table  2 ) (p-value < 2e-16).
More precisely, the proportion of bell-shaped trends is significantly greater than that of decreasing and increasing trends for defense biomarkers (Tables 3A-C). For clarity, here and in the rest of the text, p-values of the models are only given in their corresponding tables. Conversely, the proportions of decreasing and increasing trends are significantly greater than the bell-shaped trends for damage biomarkers (Tables  3A-C ). The same observation was made for the proportions of U-shaped trends compared with those of decreasing and increasing trends (Tables 3B-D). 3. 5. Response trends of biomarkers according to the type of contaminant: organic or inorganic A total of 1,321 observations in the meta-analysis database relates to the effect of organic contaminants and 1,274 to the effect of inorganic contaminants.
Implications for the use of metabolomics in ecotoxicology Part of the biomarkers compiled in the present meta-analysis are metabolites of low molecular weight. With the increase use of metabolomics and non-targeted approaches in particular, a large number of metabolites as well as their variation can be measured in organisms upon exposure to a contaminant. However, the identification of these metabolites remain challenging as many metabolites remain unannotated in the currently available databases  [13]. Assessing their variations via the use of tools such as DRomics for example  [17]  and analysing their dose-response trends such as performed in the present meta-analysis is an innovative way to evaluate stress in organisms without losing the interest of omics studies and their large number of data  [18].