dataesr/scientific-paragraphs-categorization huggingface dataset:
Other Data Collection The clinical and demographic characteristics of the patients were collected from medical records: gender, age, main diagnosis, diseases that may impact the patient's nutritional status (dementia, diabetes, organ failure, depression, cardiovascular disease, cancer, systemic disease), level of patient mobility (bedridden, semi-bedridden (including patients who can move independently in a wheelchair) or mobile) and swallowing disorders. The following information regarding treatments was collected: number of pills per day, number and nature of vitamins and/or minerals, drift of glucose and/or physiological saline. Additional information regarding diet was collected: high-protein and high-calorie diet and oral nutritional supplements.
Ethical Considerations The study obtained the agreement of the CHU Rennes Clinical Ethics Committee No. 22. 28 as a non-interventional study. Meal photography does not require any contact with the patients, and assessment of nutritional status is part of the recommended routine care in France. All the data collected were anonymized. An information notice was distributed allowing non-opposition of residents, their relatives or legal representatives. The study results will be communicated to the nursing home team and could lead to modifications in the professional practices around food.
The cut-off was chosen by maximizing sensitivity and specificity using the Youden index (Y = Sensitivity + Specificity -1). ROC curves were analyzed with their area under the curve (AUC) and 95% confidence interval (95% CI). Discriminative power of AUC was determined according to the following classification: 0. 90 ≤ AUC ≤ 1. 0, excellent; 0. 80 ≤ AUC < 0. 90, good; 0. 70 ≤ AUC < 0. 80, fair; 0. 60 ≤ AUC < 0. 70, poor; 0. 50 ≤ AUC < 0. 60, fail. Sensitivity, specificity, positive (PPV) and negative (NPV) predictive values as well as 95% CI were calculated. Spearman test was used for the correlation analyses. All statistical tests have a significance level of 0. 05. Statistical analyses were performed using SAS software, v. 9. 4 ® (SAS Institute, Cary, NC, USA).
Acknowledgments: The authors want to thank  Marie-Laure Thépot-Clément ,  Audrey Merlin  and  Marie-Christine Le Gall  for their help in the completion of the study. They also want to thank all the team of the Hôtel Dieu nursing home,  CHU Rennes, Rennes, France  for their help, flexibility and patience despite the organizational constraints linked in the study, and of course all the residents and their families.
Therefore, we used data from the recent national ODIACOR registry to determine the number of patients potentially eligible for icosapent ethyl treatment among a representative sample of CAD patients in daily practice. Methods. This prospective national database analysis included consecutive patients with documented type 2 diabetes (T2D) and CAD consulting private practice cardiologists registered with the National College of French Cardiologists (CNCF). Consecutive patients consulting between March 2019 and December 2020 were eligible for inclusion in the registry. In accordance with current regulations, this registry was declared to the Institut Nationale des Données de Santé (the national health data institute). Data were collected on a dedicated website managed by the CNCF, using an electronic case report form (eCRF).
The eCRF recorded: (1) patient data, namely: clinical characteristics, date of consultation; (2) healthcare provider data, namely: occupation, specialty, place of practice; (3) claims data from private clinics, namely: drugs prescribed, medical procedures performed, biological procedures prescribed, medical devices used, date of examinations/procedures; (4) hospitalization data, namely: date of index event, coronary revascularisation, recurrent cardiovascular events. Results. Among a total of 574 patients with CAD and T2D included in the analysis, the mean age was 71 years, and the majority (81%) were male, with a high prevalence of other risk factors (81% with hypertension) and numerous cardiovascular comorbidities, e. g. history of myocardial infarction in 47%, coronary revascularization in 78% and peripheral arterial disease in 16%.
This is a critical point as therapeutic targets may differ between human and animals. Surprisingly, a detailed comparative analysis between cachectic human patients and cachectic cancer animals is missing. This review provides a comprehensive analysis of the molecular mechanisms involved in cancer cachexia-associated muscle mass loss in preclinical and clinical studies. Article database of the US National Library of Medicine (PubMed) were searched using 'cancer cachexia' AND (muscle or 'skeletal muscle') AND specific terms (corresponding to the studied scientific area of each section). Any additional relevant literature was obtained from the reference lists of the published papers. This methodology has been applied for each section of the current review. Preclinical and clinical studies dealing with cancer, but for which cachexia was not characterized or demonstrated, were not included.
They use successive steps including data acquisition, feature extraction, data processing, differential analysis, compound annotation, and identification  [15]. Various statistical and visualization tools are used for differential analysis. Unsupervised tools such as principal components analysis (PCA) and hierarchical clustering, as well as supervised tools such as partial least squaresdiscriminant analysis (PLS-DA)  [16, 17]  can be used to visualize patterns in datasets. In addition, statistical tests such as t-tests and univariate analysis of variance (ANOVA) can be performed. A common visualization for comparing two groups is the volcano plot that represents the fold change of signal abundances between the groups versus the p-value, the latter being a criterion to assess the statistical significance of signal differences between Metabolomic approaches are able to detect a large number of molecules and to recognize compounds, discriminating different types of samples.
They use successive steps including data acquisition, feature extraction, data processing, differential analysis, compound annotation, and identification  [15]. Various statistical and visualization tools are used for differential analysis. Unsupervised tools such as principal components analysis (PCA) and hierarchical clustering, as well as supervised tools such as partial least squares-discriminant analysis (PLS-DA)  [16, 17]  can be used to visualize patterns in datasets. In addition, statistical tests such as t-tests and univariate analysis of variance (ANOVA) can be performed. A common visualization for comparing two groups is the volcano plot that represents the fold change of signal abundances between the groups versus the p-value, the latter being a criterion to assess the statistical significance of signal differences between groups, in order to recognize the best discriminating compounds  [15] [16] [17].
Color Classification Based on Visual Perception The chocolate surfaces were not reproducible from one sample to another, yielding a variability in the light scattering which could alter the color perception in a sensory analysis of the chocolates bars. Thus, another more objective approach was undertaken. The L*, a*, and b* values were used to create an image of the color of each chocolate sample with Adobe Photoshop ® software  [24]. A sensory analysis was performed on the color images of each chocolate. The images were transferred to a Microsoft PowerPoint slide and then distributed to 6 evaluators. All volunteer evaluators are employees of our research institute. They declared having no known deficiency that could influence color perception and had no difficulty recognizing colors in everyday life.
Chemometrics The HRMS data from the S1 series were processed using 3. 2 Compound Discoverer software. The values of the processing parameters of the UHPLC-HRMS data are described in the supplementary material (text document). The data processing resulted in a dataset where the rows corresponded to 371 features, each characterized by a retention time (RT) and m/z value. Each column presents the abundances of each feature of a sample. First, unsupervised PCA was performed on the dataset using the 3. 2 Compound Discoverer software, applying the center and scale options and normalized areas. Then, a volcano plot was used for feature selection. It compared two groups, brown versus black chocolates, for each feature. It was built as follows. The fold change was determined as the ratio of the brown/black mean areas, and a log 2 transformation was applied to the fold change.
The FBMN analysis was carried out as follows. The MS and MS/MS data in raw format obtained from the S2 analysis series were converted to. mzML format, using the MSConvert tool (version 3). Data were then processed with MZmine 3  [28]  using processing parameters whose values are described in the supplementary material (text document). The results of this processing consisted of the feature quantification table (. CSV file format) containing the abundances of 562 LC-MS features, and the MS/MS spectral summary (. MGF file) with a representative MS/MS spectrum per LC-MS feature. These files were exported to GNPS  [29]  for FBMN analysis from the FBMN workflow  [19]. The precursor ion mass tolerance was set to 0. 02 Da and the MS/MS fragment ion tolerance to 0. 02 Da. The molecular networks were then created where edges were filtered to have a cosine score above 0.
The rest of the chocolate samples were classified having an intermediate color and were eliminated from the further stages of the study. Figure  2  shows the reconstituted colors of the black and brown chocolate groups selected by the evaluators. tral summary (. MGF file) with a representative MS/MS spectrum per LC-MS feature. These files were exported to GNPS  [29]  for FBMN analysis from the FBMN workflow  [19]. The precursor ion mass tolerance was set to 0. 02 Da and the MS/MS fragment ion tolerance to 0. 02 Da. The molecular networks were then created where edges were filtered to have a cosine score above 0. 7 and more than 6 matched peaks. The molecular networks were visualized using Cytoscape software (version 3. 9. 1) [30]. Manual annotation of features was performed based on predicted molecular formulae, fragmentation patterns, and comparison to literature.
Data Processing and Statistical Analysis The 16 selected black and brown chocolate samples (Table  S2 ) were then analyzed by UHPLC-ESI-Q-Orbitrap MS. Features from the UHPLC-HRMS data were extracted by the 3. 2 Compound Discoverer software, resulting in a dataset of 371 features. Table  S3  shows the values of RT and measured m/z of the selected features as well as their chromatographic peak areas for each sample. The score plot of the PCA performed on the dataset shows good separation between the brown and black chocolate groups (Figure  3 ). Principal components 1 and 2 (PC1 and PC2) accounted for 31. 2% and 15. 9% of the variability, respectively. Both PC1 and PC2 contributed to this separation. In general, black chocolates were characterized by low scores on both PC1 and PC2 whereas brown chocolates were characterized by higher PC1 and PC2 scores for most samples.
Then, the same dataset (Table  S3 ) was used to obtain the volcano plot (Figure  4a ) for selection of the most discriminating features in the two groups. According to the fold change calculation described in the Experiment section, overaccumulated compounds of brown and black chocolates were located on the right and left hand sides of the graph, respectively. For example, for a value of +3 on the abscissa axis of a given feature, the mean area of brown chocolates is 2 3 = 8 times higher than that of black chocolates; and for a value of -3, the mean area of brown chocolates is 2 -3 = 1/8 of that of black chocolates. Although no regulation mechanism is presented in our study, by analogy with terms introduced in the literature  [15] [16] [17] , features on the right hand side would be upregulated for brown chocolates but downregulated for black chocolates and features on the left hand side would be upregulated for black but downregulated for brown chocolates.
, USA) of 50 m×0. 25 mm i. d coated with 0. 25 µm film thickness. Peaks of SCFAs were quantified using XCalibur QuanBrowser software (Thermo Fisher Scientific, Illkirch, France). Details on chromatography, calibration range, limit of detection and retention time of SCFAs are reported in supplementary materials (Supplementatary Table  S1 ).
Statistical analysis Data are presented as mean ± SEM. Statistical analysis were performed with GraphPad Prism 7. 0. software (San Diego, USA). Grubb's test was used to detect any outliers. Normality of variables was evaluated by a D'Agostino-Pearson test. Statistical differences were then tested by One-way Anova (Tukey's posttest) or Kruskal-Wallis (Dunn's post-test) according to sample normality assessment results. A p-value <0. 05 was considered significant.
Data sources The analysis presented here pools twelve cohort study datasets from twelve different low-income countries with data on children aged between 6 and 59 months collected between 1977 and 2013. The cohorts span countries in Africa, the Americas, Asia and the Pacific. Anthropometric measures were taken on children in the cohorts over periods ranging from 0 (when only a single anthropometric measurement was taken) to 37 months with a median of 12 months. All studies were large prospective cohort studies of children, or follow-ups of randomised trials examining various interventions and relationships including health and nutrition outcomes after vitamin A supplementation, health and nutrition outcomes after treatment with azithromycin for trachoma control and the distributions of mortality risk attributable to low nutritional status.
All datasets were population-based samples of children selected based on age. In no case were the children selected based on any particular health or nutrition condition. Ten of these datasets were obtained by contacting the principal investigators of the studies featured in an earlier publication on single and multiple anthropometric deficits and mortality  (16). Permission to use the data was provided in all cases and access was given to original datasets (India and Nepal) or to datasets that were used in the earlier pooled analyses (all others). This dataset has been described in detail elsewhere  (22) (23) (24) (25) (26) (27) (28) (29). Two additional datasets were obtained through personal contacts providing mortality cohort data from the Democratic Republic of Congo (DRC) and from Niger and are also described in detail elsewhere  (30) (31) (32).
All data apart from that from Niger (2013) were collected in contexts and timeframes where widespread CMAM programming was not in place. All data were de-identified. The characteristics of the included data in terms of location, study identifier, recruitment dates and age at recruitment as well as the numbers of children, follow-up periods and deaths within 6 months of measurement are shown in Table  1. Details on loss to follow-up were recorded in some, but not all, of the original cohorts. For the cohorts where reasons for loss to follow-up were recorded and reported on in the original study papers, outmigration was the most common reason given. See Table  1.
Data management Datasets were harmonised into a standard structure to contain the same variables with the same names, types, lengths, units and codes. Therapeutic programmes currently treat severe acute malnutrition primarily in children aged between 6 and 59 months with a standard protocol using ready-to-use therapeutic foods. Although community-based management of wasting in infants under 6 months of age is recommended by WHO, in practice, treatment is still often mainly inpatient based and active case finding is not common. The appropriate indicators to identify those most at risk of death and growth faltering and community-based management strategies are a focus of an active global community of policymakers, researchers and practitioners (MAMI Global Network) and hence merit separate analysis and discussion.
The 6-59-month age group currently represents the vast majority of children treated in such programmes, therefore the analysis used only data for children in this age group at the time of anthropometric assessment. HAZ, WAZ and WHZ were calculated using the 2006 WHO growth standards  (33). Records with extreme Z-score values were identified and censored using the WHO 'biological plausibility' criteria  (34). Records with MUAC < 70 mm or > 240 mm were also censored on the grounds of biological implausibility. MUAC was measured and collected in four of the twelve cohorts (DRC, Nepal, Senegal and Niger). The outcome of interest was near-term mortality defined as death within 6 months of anthropometric assessment in line with previous similar analyses  (21). The units of analysis used are the 6-month period following an anthropometric measurement.
Sensitivity, specificity and informedness The performance of different anthropometric indices and the twelve anthropometric case definitions for predicting death within 6 months of anthropometric assessment was evaluated by estimating sensitivity and specificity and plotting receiver operating characteristic curves. A measure of informedness (the probability of an informed decision being made as opposed to a random guess) combining both specificity and sensitivity in a single statistic (Youden's index or Youden's J statistic) was also calculated for each case definition  (35)  : J ¼ Sensitivity % ð Þ þ Specificity % ð Þ À 100 Cohort-specific results were combined using random effects (DerSimonion-Laird) meta-analysis. Analyses including the MUAC case definition (or where MUAC was a component of the case definition) were restricted to the four cohorts that collected MUAC data (DRC, Nepal, Niger and Senegal).
Inclusivity The possibility of identifying all, or nearly all, children with severe anthropometric deficits who are likely to die within 6 months of measurement was explored using a sets-based analysis. This used four-dimensional Venn diagrams with sets defined by the WAZ < -3, WHZ < -3, MUAC < 115 mm (when MUAC data was present) and the WaSt case definitions. Three-dimensional Venn diagrams (with WHZ < -3, WAZ < -3 and WaSt) were used when MUAC was absent from a cohort dataset. We included WHZ-based case definitions as WHZ < -3 is currently used in many national and international therapeutic feeding guidelines and in emergency settings by international non-governmental organisations. The ability of moderate case definitions (WAZ < -2 and MUAC < 125 mm) to identify children with severe anthropometric deficits who are likely to die was also explored for completeness/comparison.
Compatibility/practicability This is the degree to which a case definition is compatible with current practices, tools and case-defining thresholds. Case definitions requiring the measurement of height might be considered to lack compatibility because the integrated management of childhood illnesses (IMCI) syllabus does not cover the measurement of height or the calculation/ lookup of the WHZ or HAZ values, height boards are often not part of routine clinic equipment, and the measure requires at least two trained health staff who are often in short supply at community level. Bilateral pitting oedema was not investigated because the relevant data was not present in the cohort datasets. It was assumed that therapeutic feeding programmes will continue to admit children using bilateral pitting oedema as an independent admission criterion due to its association with mortality and its response to treatment.
Prevalence was modelled using the 25th, 50th and 75th percentiles of prevalence estimates for the MUAC and WAZ components of the selected case definition using data from 2 426 nutritional anthropometry surveys from fifty-one countries from 1992 to 2015  (17). Coverage for MUAC was modelled using the 25th, 50th and 75th percentiles of coverage estimates from 227 coverage assessments of CMAM programmes from twenty-nine countries from 2009 to 2017 recorded in a database provided by the Coverage Monitoring Network  (21). It was assumed that cases with WAZ < -3 could be identified by growth monitoring or growth monitoring and promotion (GM/GMP) programmes with appropriate referral  (21). A literature review was undertaken and found useable coverage data for twenty-three GM/GMP programmes of differing scope and design from fourteen countries  (21).
Using anthropometry to predict mortality Sensitivity, specificity and informedness Table  2  shows pooled sensitivity, specificity and informedness (Youden's Index) for twelve anthropometric case definitions and all-cause mortality in twelve cohort datasets (four cohorts for MUAC). The analysis is split to show results by age group <24 months of age and ≥24 months (eight cohorts with data on children aged ≥24 months). The table indicates that for children aged 6-59 months for all case definitions, specificity comes at the cost of sensitivity. Unsurprisingly, the most sensitive (i. e. identifying the most children who go on to die) measures, WAZ < -2 and HAZ < -2 are the least specific (misidentify the most children who are not going to die). However, four case definitions, (WAZ < -2), (MUAC < 125 mm), (MUAC < 115 mm or WAZ < -3) and (WAZ < -3) had the highest informedness (Youden's Index) of all the tested case definitions.
Finally, the question of the factors that are driving both wasting and stunting in these children at highest mortality risk is outstanding and remains the focus of other ongoing work  (48). A particular strength of our study is that it is based on the analysis of a large number of available datasets from different contexts with anthropometric indices and mortality in community cohorts of untreated children. Due to the increased coverage of community-based management of acute malnutrition interventions globally over the last decade, more recent cohorts do not allow examination of the risk associated with anthropometric indicators in the absence of treatment. A large sample size is needed to study the relationship between anthropometric case definitions of severe undernutrition and the risk of death which are, from the statistical point of view, rare events.
As such, this dataset is able to provide information regarding the prognostic value of anthropometric case definitions for identifying mortality risk in children across a range of populations. While this study does not include infants under 6 months, where the most predictive case definitions may be different, recent published work by others suggests that WAZ will be a better indicator than WHZ or WaSt for that age group  (49). As for older children, WAZ is easier to measure than weight for length in infants and, because the weight for length standards do not have Zscores for infants <45 cm, WAZ will better capture mortality risk by including the most severely stunted neonates who may have low but incalculable weight for length  (50). There are also several limitations of the analysis presented in the paper.
A potential bias may be introduced by loss to follow-up in the original studies masking deaths within the cohorts. Despite examination of the original papers (see Table  1 ), it was not possible to quantify this. It is also not possible to quantify the possible bias introduced by the absence of data on oedema which may affect the prognostic value of both WAZ and WHZ where it was present. It is also worth noting that the mortality results recorded are all-cause, as information on the cause of death was not consistently recorded between and within datasets. This means it is not possible to specifically attribute cause of death to anthropometric status. We can only ascertain whether anthropometric status is predictive of death. It is also possible that the monitoring of children in the cohorts and the potential referral for medical treatment may have led to lower mortality rates than in non-studied peers and may, therefore, underestimate relative risks.