dataesr/scientific-paragraphs-categorization huggingface dataset:
FU = follow-up. X = presenting the availability of the data collection for each follow-up. For our study, only children aged 0-12 months at the 1st FU were selected to investigate their feeding practices for the first year of life. Out of these 2418 children, only those with complete data on feeding practices and anthropometric measurements were included for analysis. Hence, the sample size for the current data analysis was 1079 children in the 1st FU, with a total number of 5984 follow up visits (Figure  1 ).
Statistical Analysis All data analysis was done using STATA software version 14 (Stata Corp LLC. , College Station, TX, USA). Descriptive analysis was performed to describe variables with a normal distribution using the mean, standard deviation, and range. Categorical variables were expressed as frequencies and percentages. A chi-square test were used to analyze the association between growth status and age, between age-appropriate feeding practices and age, and between growth status and adherence to age-appropriate feeding practices. To determine the relationship of adherence to age-appropriate feeding indicators and other potential determinants with child's growth, generalized linear mixed-effects models were done. Linear mixed-effects were used for continuous outcomes (HAZ and WHZ), and mixed-effects logistic regression was done for binary outcome (WaSt).
Sensitivity analyses were performed to check the validity and consistency of the models. The selection criteria for sensitivity analyses were similar as our main analyses. The difference between them is that we included children for whom data collection regarding the feeding practices was not fully complete (one or more follow-ups were missing). Generalized linear mixed-effects models were done to determine the association between adherence to age-appropriate feeding practices and child growth. Fixed effects included regions, age, gender, age-appropriate feeding practices, caretaker's education level, wealth index, child's nutritional status, household main drinking water source, treatment of drinking water, and ownership of latrine. The child's ID with random slope of child's age and nested by regions were selected as random effects.
Protein biomarker data The protein biomarker data used for this study have been described in recent papers. 4, 5 In brief, we used the Cardiovascular III (CVDIII) panel of 92 cardiovascular disease-related proteins provided by Olink Bioscience analysis service (Uppsala, Sweden). The proteins were profiled using the Olink Proseek ® Multiplex Inflammatory 96*96 platform (details in online supplementary material and supplementary Table  S1 ).
Differential protein expression analyses Differential protein expression analysis was done by simply tethering the fold change of the 92 protein biomarkers in ischaemic vs. non-ischaemic patients for the index and validation cohorts. A minimal fold change of 1. 15 yielded consistent results between patients in the index and validation cohort (online supplementary Figure  S1 ). This threshold was subsequently applied to a more complicated differential expression analysis using the Linear Models for Microarray data analysis (Limma) 6 software (version 3. 34. 9). Analyses were done at a fold change threshold of 1. 15, P-value and false discovery rate (FDR) <0. 05. Proteins that met these cutoffs were considered differentially expressed in ischaemic relative to non-ischaemic HF patients. In practice, the P-value is lower than the FDR, so effective differential expression hits are determined by the fold change and FDR.
Construction of enriched physical protein-protein interaction networks We constructed a comprehensive biological network of human physical protein-protein interactions (NR_HsapiensPPI; details in online supplementary material), consisting of 17 625 unique nodes with 330 157 interactions. These protein-protein interactions are not based on in-silico predictions, but are rather biologically determined and manually curated and stored in secondary databases used in this manuscript. We have rendered these into current terms to enable integration with contemporary data analyses to ensure minimal information loss. [9] [10] [11] [12]  From the general 'NR_HsapiensPPI' network, we harvested an 'Olink PPI network' as the subnetwork that contains nodes (and associated interactions) belonging to any of the 92 Olink protein biomarkers.
Baseline characteristics From the 2516 HF patients of the BIOSTAT-CHF index cohort we identified 715 patients with Olink-CVDIII data who met the stringent criteria for ischaemic HF and 445 patients with Olink-CVDIII data who met the stringent criteria for non-ischaemic HF. Patients in both groups were mainly men (82% in the ischaemic group and 73% in the non-ischaemic group; P < 0. 001). Baseline characteristics of ischaemic vs. non-ischaemic HF patients are shown in Table  1. Ischaemic patients were older, had significantly lower heart rates and a more frequent history of HF hospitalization and smoking. In both study groups, most patients had either HF with reduced or mid-range LVEF and there was no significant difference in the prevalence of HF with preserved LVEF between the groups with ischaemic and non-ischaemic HF.
Therapies targeting the five key proteins Amongst the five key proteins found, increased levels of ACP5 and EGFR were associated with improved clinical outcome while IGFBP1, PLAUR and SPP1 were associated with worse clinical outcome. In the Therapeutic Target Database 36 (available at: db. idrblab. net/ttd/), EGFR has been targeted (by e. g. cetuximab, an antibody agent) for the treatment of inflammatory breast cancer and colorectal cancer. The other four remain to be tested. Knock-out models or other perturbation of levels for these five targets would potentially enhance our understanding of their involvement in ischaemic heart disease. ACP5 [also known as tartrate-resistant acid phosphatase (TRAP)] is involved in bone remodelling, a process that is gaining increasing interest in arterial calcification.
Conflict of interest: A. A. V received consultancy fees and/or research grants from  Amgen ,  Applied Therapeutics. AstraZeneca ,  Bayer ,  Boehringer Ingelheim ,  Cytokinetics ,  GSK ,  Myokardia ,  Novacardia ,  Novartis ,  Roche Diagnostics, Servier. C. S. P. L. is supported by a  Clinician Scientist Award  from the  National Medical Research Council of Singapore ; has received research support from  Boston Scientific, Bayer ,  Roche Diagnostics ,  AstraZeneca ,  Medtronic , and  Vifor Pharma ; has served as consultant or on the  Advisory Board/Steering Committee/Executive Committee.
The sequence obtained can first be matched to large reference databases that cover almost the entire known bacterial diversity, but final validation is preferably done using curated databases such as PATRIC  (Wattam et al. , 2017). In the case of doubt or for a more detailed identification, involving other molecular and phenotypic traits, Bergey's Manual of Determinative Bacteriology can be consulted. 1 Further,  Mattarelli et al. (2014)  described the "Recommended minimal standards for description of new taxa of the genera Bifidobacterium, Lactobacillus and related genera. " This is a useful resource for techniques that can be applied to proper identification as well. The "gold standard" for strain identification is whole genome sequencing (WGS) including any extrachromosomal elements. A fully sequenced genome allows the identification of microbes to the species and strain level.
Reference databases are available, e. g. , at NCBI. 2  Having a complete genome sequence has many advantages as it allows robust and precise strain specific identification and facilitates a search for the presence or absence of risk factors (see section "Probiotic is Safe for Intended Use" on safety). Furthermore, it will help to identify possible plasmids, common in some lactic acid bacteria and possibly important for probiotic activity. While WGS is the preferred method, other typing methods may allow comparison of individual strains. Multi-locus sequence typing or pulsed-field gel electrophoresis allow comparison of strains, but not de novo identification of species or genus. Identification should preferably be done by specialized or appropriately accredited laboratories, which can access the required reference databases and use current validated and calibrated methods and equipment.
PROBIOTIC IS SAFE FOR INTENDED USE Providing consumers with foods and dietary supplements that meet applicable safety standards is a basic responsibility of probiotic manufacturers. Establishing that a specific probiotic is safe for use in foods and dietary supplements requires, as a starting point, proper identification to the strain level, and further documenting safe use through historical evidence or experimentation. Historical data of safe use can be an important factor in an overall assessment of safety for an intended use. In its absence, safety must be determined based on scientific principles, including the conduct of adequate phase 1 studies  (Brodmann et al. , 2017). Strain safety is assessed on a case-by-case basis and no specific requirements for sufficient evidence can be made, but a probiotic strain needs to comply with the safety requirements stipulated by the national/regional regulator as discussed below.
If resistance above cut-off values is observed, further characterization is required. WGS of the strain will confirm the presence or absence of known genes involved in the observed resistance. In cases where putative resistance genes are detected, it is recommended to determine if transposable elements are in their genomic vicinity. If this is the case it cannot be excluded that the resistance gene is transferable, and commercialization of the strain is not recommended. Otherwise, the genome sequence can assist in identifying putative antimicrobial resistance genes by searching at least two databases. For microorganisms not well-represented in databases, a Hidden-Markov model database is recommended. Depending on their taxonomy and their intended use, the strain's genome may need to be assessed for the presence of genes coding for known virulence factors such as toxins, invasion, and adhesion factors (EFSA, 2020a).
In those cases where antibiotic resistance cut-off values are not known, it would be the responsibility of the producer to ensure that the proposed probiotic strain(s) do not contain transferrable antibiotic resistance genes, and that the resistance profile is consistent with other members of the same species. In some cases it may be necessary to generate new data on the susceptibility profiles of the considered taxon, including making sure that susceptibility testing methods are relevant and adapted to the physiology of the considered micro-organisms. Other phenotypic properties may be assessed at strain level for safety, such as the ability to form biogenic amines and D-lactate. Both can be conveniently tested through analysis of the genome or through standardized phenotypic tests. In addition, hemolytic activity and bile salt hydrolase activity are sometimes assessed at strain level, but their relevance to safety remains to be determined  (Huys et al.
The "Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT)" 2013 checklist contains a list of 33 elements that should be included in all clinical trial protocols  (Chan et al. , 2013). In accordance with the SPIRIT 2013 checklist (Item 2), the publication of the study protocol in a public database (e. g. , ClinicalTrials. gov) prior to the start of the study is highly recommended 3 and is viewed as a way of fostering the design of higher-quality studies while contributing to more transparent reporting of results. Furthermore, the publication of clinical trial protocols in peer-reviewed journals, which usually require 3 http://www. icmje. org/news-and-editorials/clin_trial_sep2004. pdf that the protocol should be registered in a public registry, also constitutes a good practice that should be further encouraged in the probiotics field.
Briefly, CONSORT covers all aspects of trial design and conduct, data collection and analyses, as well as reporting. For example, CONSORT requires a participant flowchart explicitly stating numbers of participants for each step from recruitment to study completion, the exclusions, losses to follow up, and sizes of the intent-to-treat or per protocol populations. This information is crucial for future quality and RoB assessments needed afterward (e. g. , systematic reviews for evidence-based medicine guidelines or regulatory purposes). CONSORT also stipulates that the authors should highlight the limitations of their study, such as the sources of bias and uncertainties that may influence the interpretation of the results. A description of results generalizability is expected, as well as a clear perspective of health benefits versus risks (implying a detailed reporting of the adverse events).
International council for harmonization of technical requirements for pharmaceuticals for human use E6 (R2) https://database. ich. org/sites/default/files/ E6_R2_Addendum. pdf Statistical principles for clinical trials E9 https://database. ich. org/sites/default/files/ E9_Guideline. pdf Structure and content of clinical study reports E3 https://database. ich. org/sites/default/files/ E3_Guideline. pdf SPIRIT 2013 https://www. spirit-statement. org/ https://www. spirit-statement. org/ publications-downloads/ Critical appraisal tools (quality assessment) JBI checklists (Joanna Briggs Institute, 2017) (Tufanaru et al. , 2017) https://joannabriggs. org/ebp/critical_appraisal_tools, https: //joannabriggs. org/sites/default/files/2019-05/JBI_RCTs_Appraisal_tool2017_0. pdf CASP checklists (Critical appraisal skills program, 2018) https://casp-uk.
Homozygotes for the higher penetrance hemochromatosis risk allele, HFE c. 845G>A (p. Cys282Tyr, or C282Y), have been reported to be at a 2-to 3-fold increased risk for colorectal cancer (CRC). These results have been reported for small sample size studies with no information about age at diagnosis for CRC. An association with age at diagnosis might alter CRC screening recommendations. We analyzed two large European ancestry datasets to assess the association of HFE genotype with CRC risk and age at CRC diagnosis. The first dataset included 59,733 CRC or advanced adenoma cases and 72,351 controls from a CRC epidemiological study consortium. The second dataset included 13,564 self-reported CRC cases and 2,880,218 controls from the personal genetics company, 23andMe. No association of the common hereditary hemochromatosis (HH) risk genotype and CRC was found in either dataset.
The odds ratios (ORs) for the association of CRC and HFE C282Y homozygosity were 1. 08 (95% confidence interval [CI], 0. 91-1. 29; p ¼ 0. 4) and 1. 01 (95% CI, 0. 78-1. 31, p ¼ 0. 9) in the two cohorts, respectively. Age at CRC diagnosis also did not differ by HFE C282Y/C282Y genotype in either dataset. These results indicate no increased CRC risk in individuals with HH genotypes and suggest that persons with HH risk genotypes can follow population screening recommendations for CRC.
Introduction The majority of adult-onset hereditary hemochromatosis (HH; MIM: 235200) cases are associated with pathogenic variants in the HFE gene (MIM: 613609; GenBank: NM_000410. 3). The highest penetrance pathogenic variant is HFE c. 845G>A (p. Cys282Tyr, or C282Y; rs1800562), which has a minor allele frequency (MAF) ~4%-6% in mixed European-ancestry populations. 1, 2 This allele is more common in Northern European ancestry populations and less frequent in Southern European ancestry populations. 3 Homozygosity for this variant accounts for ~85% of individuals with HH. 4, 5  A more common European ancestry allele is rs1799945C>G (p. Hi-s63Asp, or H63D; rs1799945; MAF ¼ 17% in the 1000 Genomes European population); however, it has lower penetrance and is infrequently associated with HH, unless found in the compound heterozygous form C282Y/H63D.
21 dataset discussed below, plus three additional papers. 20 he association of clinical HH and CRC has not been studied in large samples, but one smaller study of 1,847 individuals with HH and 5,973 of their first-degree relatives found no association. 17 The reported HFE genotype association with CRC is based on small sample sizes  [18] [19] [20] [21]  and has not been incorporated into clinical genetics or gastroenterology care. Additionally, the possible relationship between hemochromatosis risk genotypes or phenotype and CRC raises the question of appropriate CRC screening ages for individuals with the risk of genotype. Previous studies have not evaluated the association of HFE genotype or clinical HH phenotype with the age at CRC diagnosis. Therefore, we evaluated both the association of HFE genotype with both CRC case versus control status and age at CRC diagnosis in two large datasets, one a large CRC epidemiological study consortium and the other a personal genomics company.
Participants, phenotyping, and genotyping The first dataset, CRC case-control (CRC C-C), includes data from The Colon Cancer Family Registry (CCFR),  22, 23  Colorectal Cancer Transdisciplinary Study (CORECT),  24, 25  and Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). 26, 27 Details about the studies, genotyping quality, and imputation are described elsewhere. 27 The CRC case-control and genotype status of this dataset is reported by platform in Table  S1. All studies were approved by their respective institutional review boards, and proper consent was obtained. Given that hemochromatosis primarily affects individuals of European ancestry, we limited our analyses to that ancestry group to reduce risk of population stratification. The dataset included 59,733 CRC or advanced adenoma cases (46.
5% female, 8. 5% advanced adenoma), of which 54,211 had age-at-diagnosis data, and 72,351 controls (49. 6% female), who had European ancestry assigned by imputation. CRC phenotype was confirmed as invasive colorectal adenocarcinoma or advanced adenoma by medical record, pathology report, or death certificate. Henceforth this phenotype will be referred to as CRC. Control participants were selected based on study-specific eligibility and matching criteria (e. g. , sex and age). HH diagnosis was not available for this dataset. Ancestry was determined genetically, using principal component analysis. HFE variants rs1800562 and rs1799945 were either directly genotyped or imputed using the TOPMed 28 panel as reference. Imputed allele dosages were converted to genotype calls using dosage <0. 5 and >1.
5 thresholds. The second dataset was drawn from research participants among clients of the personal genetics company, 23andMe (Sunnyvale, CA, USA). Participants provided informed consent and answered online surveys under a protocol approved by Ethical and Independent Review Services, an independent Association for the Accreditation of Human Research Protection Programs (AAHRPP)-accredited institutional review board. CRC phenotype was determined by self-report response to the online survey question ''What type(s) of cancer were you diagnosed with or treated for? Please select all that apply:'' with the selection of ''Colon/colorectal cancer. '' Self-report of colon cancer has been reported to be reasonably accurate in prior studies. 29, 30 Similarly, HH diagnosis phenotype was assessed by self-report (''Have you ever been diagnosed with, or treated for, hemochromatosis?'') and contrasted with HFE genotype and self-report of phlebotomy (''Did your doctor ever remove your blood regularly in order to treat hemochromatosis?'').
The 23andMe CRC dataset was restricted to 2,893,782 participants inferred to be of greater than 97% European ancestry, based on local ancestry, with age between 30 and 90 years. It included 13,564 CRC cases (50. 1% female) and 2,880,218 non-CRC controls (55. 5% female). A total of 11,270 participants reported HH, of whom 89 also reported CRC. HFE genotypes for rs1800562 and rs1799945 variants were determined by five different Illumina genotyping platforms (call rate > 99. 95% for both variants). DNA extraction and genotyping were performed on saliva samples by LabCorp.
Statistical analyses We conducted a CRC case-control analysis in the CRC C-C dataset using logistic regression to estimate the OR of CRC associated with the exposure of C282Y/C282Y genotype versus no HH risk allele genotype (WT/WT), adjusted for age, sex, genotyping platform, and 3 ancestry principle components. Our analysis assumes the known recessive model for HH and only considers the highest penetrance risk genotype, which is expected to have the largest effect size. A secondary analysis contrasted participants with either C282Y/H63D or C282Y/C282Y risk genotypes to participants with no risk allele (WT/WT). Given that hemochromatosis penetrance is higher in males, sex-specific sensitivity analyses were also performed. We tested whether HFE C282Y/C282Y genotype is associated with age at CRC diagnosis using a two-sample t test.
Similar analyses were undertaken in the 23andMe dataset. Logistic regression was used to predict the outcome of prevalent CRC status for the exposure of C282Y homozygosity versus no risk allele (WT/WT); covariates included age, sex, genotyping platforms, and ancestry principle components. To test the association of C282Y homozygosity versus WT/WT on age at CRC diagnosis, Cox proportional hazard analysis was performed.
CRC C-C dataset The OR of CRC risk, including covariates age, sex, genotyping platform, and 3 ancestry principle components, was 1. 08 (95% CI, 0. 91-1. 29; p ¼ 0. 39; see Table  1  for summary of CRC-HFE genotype association tests). The analyses included 580 participants who were HFE C282Y homozygotes and 114,053 participants who had no C282Y or H63D alleles (WT/WT genotype; Table  S1 ). The analysis had 80% power to detect an OR of 1. 24. Stratified analyses by sex identified no increased risk in males (OR, 0. 94; 95% CI, 0. 73-1. 21; p ¼ 0. 64) or females (OR, 1. 22; 95% CI, 0. 95-1. 57; p ¼ 0. 12). Secondary analyses combining N ¼ 2,307 participants who were C282Y/H63D compound heterozygotes (967 CRC cases and 1,340 controls) with the C282Y homozygotes versus WT/WT also detected no association of genotype with CRC risk; this yielded an OR of 1.
00 (95% CI, 0. 92-1. 08; p ¼ 0. 99). The   2 ). We considered testing the association of self-reported HH with CRC in the 23andMe dataset; however, evaluation of the self-reported data suggested poor phenotype quality. 23andMe participants' self-report of HH phenotype was compared with HFE C282Y and H63D allele counts. Of the 89 participants who reported having both CRC and HH, only 39 had genotypes consistent with an HH diagnosis (20 C282Y/C282Y, 12 C282Y/H63D, and 7 H63D/H63D), and 50 (52. 2%) did not have genotypes consistent with the HH diagnosis (20 WT/WT, 20 WT/ H63D, and 10 WT/C828Y). Of the 11,178 reporting HH but not CRC, 6,131 (54. 9%) did not have compatible genotypes. While there are rare non-HFE causes of HH in adults and rare HFE variants that were not genotyped here that can cause HH, it is likely that the majority of these participants without a risk genotype are misreporting HH or misdiagnosed, as HH rarely occurs in WT/WT European ancestry individuals.
Discussion Our analyses, in two very large datasets, detect no association of HFE C282Y/C282Y genotype with CRC. The upper limit on the 95% CI of the OR in both cases was ~1. 3. While we cannot rule out a more modest association of this genotype with CRC, these results conflict with prior reports of 2-to 3-fold excess risk or the 2016 meta-analysis estimate of an OR of 1. 7. 20 Our secondary analyses of HFE C282Y/C282Y or C282Y/H63D genotype versus WT/WT genotype also did not find an HFE genotype association with CRC. The less-penetrant H63D allele has generally not been associated with CRC risk,  20, 32  and we also did not see any association of this allele with CRC in our analyses. Additionally, we found no association of HH risk genotype with age at CRC diagnosis, consistent with the lack of HFE CRC association.