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> Disorders > Disease or Syndrome > Hemorrhoids

Hemorrhoids

Hemorrhoids, also known as piles, are a common condition involving the swelling of veins in the lower rectum and anus.
They can cause symptoms such as itching, bleeding, and discomfort.
Pubcompare.ai's AI-driven protocol comparison tool allows you to effortlessly locate and analyze the best treatment protocols from literature, preprints, and patents, helping you optimize your hemorrhoids research.
Achieve reproducibility and identify the most effective hemorrhoid products with this powerful platform.
Experiance the future of evidence-based medical research today.

Most cited protocols related to «Hemorrhoids»

1
Detecting Sequencing Errors in Nipponbare Genome
To detect small sequencing errors (single nucleotides and indels of 1–4 bases) in the newly assembled reference genome, Illumina Genome Analyzer II/IIx generated reads were used. The genomes of two different individuals of Nipponbare were independently re-sequenced at NIAS and CSHL. Low quality bases (http://hannonlab.cshl.edu/fastx_toolkit/). Reads that were <32 bp in length were discarded for further analyses. If only one read of a paired-end read set was discarded in these preprocessing steps, the other read was regarded as a single-end read and named "unpaired." All qualified reads were aligned to the reference genome using BWA v0.5.8a with default options (Li and Durbin 2009 (link)). The NIAS single-end reads and CSHL unpaired reads were aligned in the single-end mode using the BWA command “samse”. The CSHL paired-end reads were aligned in the paired-end mode using the BWA command “sampe”. The reads that matched to multiple genomic positions were discarded. A pile up alignment file of all uniquely mapped reads with a mapping quality value of ≥20 was generated using SAMtools v1.8 (Li et al. 2009 (link)). To avoid erroneous detection of variants, only sites with a read depth of 10 or more were selected.
By comparing the Illumina reads with the reference genome, each aligned site was first classified into four categories: "reference type (R)," "non-reference type (N)," "allelic (A)," and "low depth (L)" for each of three sets (NIAS, CSHL and NIAS + CSHL) (Additional file 7). If a site had less than 10 reads, the site was "low depth (L)," which means we were unable to assess the site due to low sampling. If ≥80% of the reads were identical to the reference base, the site was classified as "reference type (R)". If ≥80% of the reads were discordant with the reference base, the site was classified as "non-reference type (N)". If there were two alleles with ≥40% read support, the site was classified as "allelic (A)". Since we have two data sets from NIAS and CSHL, the classifications of the three sets (NIAS, CSHL and NIAS + CSHL) were combined and reexamined to decide the genotype for each site (Additional file 7): "reference type", "sequencing error (Additional file 9)", "alleles between individuals” (Additional file 10), "alleles within individuals” (Additional file 11), and "low depth". SNPs classified as allelic variations were annotated based on the RAP-DB gene models using SnpEff v. 3.1 (Cingolani et al. 2012 (link)) (Additional file 12).
The genome of the same NIAS individual used in the Illumina re-sequencing was sequenced using the Roche GS FLX platform. Low quality bases (http://www.repeatmasker.org/) with the MIPS Repeat Element Database (mips-REdat) version 4.3 (http://mips.helmholtz-muenchen.de/plant/genomes.jsp; Spannagl et al. 2007 (link)) and the Triticeae Repeat Sequence Database release 10 (http://wheat.pw.usda.gov/ITMI/Repeats/). All preprocessed reads were aligned to the reference genome using Megablast (version 2.2.24) with the following options: -F 'm D' -U T -e 1e-10 (Zhang et al. 2000 (link)).
Kawahara Y., de la Bastide M., Hamilton J.P., Kanamori H., McCombie W.R., Ouyang S., Schwartz D.C., Tanaka T., Wu J., Zhou S., Childs K.L., Davidson R.M., Lin H., Quesada-Ocampo L., Vaillancourt B., Sakai H., Lee S.S., Kim J., Numa H., Itoh T., Buell C.R, & Matsumoto T. (2013). Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data. Rice, 6, 4.
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Publication 2013
Alleles Genes, vpr Genome Genome, Plant Genotype Hemorrhoids INDEL Mutation Macrophage Inflammatory Protein-1 MPIF-1 protein, human Nucleotides Single Nucleotide Polymorphism Triticum aestivum
2
Applying Coolpup.py to Hi-C Data
As a proof of principal, we applied coolpup.py to publicly available Hi-C data (Bonev et al., 2017 (link); Nora et al., 2017 (link)) using distiller (https://github.com/mirnylab/distiller-nf) to obtain .cool files filtered with a map quality (mapq) of ≥30. We used these data at 5 kb resolution. In addition .cool files for single-nucleus Hi-C (snHi-C), together with coordinates of loops and TADs used in the original publication (kindly shared by Hugo Brandão) (Gassler et al., 2017 (link); Rao et al., 2014 (link)), were re-analysed at 10 kb resolution (without balancing and with coverage normalization and 10 random shifts). We also used single-cell Hi-C data for mouse ES cells grown in serum from Nagano et al. (2017) (link) (.cool files were kindly shared by Aleksandra Galitsyna) at 5 kb resolution. We created pile-ups for each cell in the same manner as for snHi-C. The pile-ups with the coefficient of variation of values in their 5 × 5 upper left and lower right corners equal to 0.5 or above were not used further as too noisy. We used the average value of interactions in the central 3 × 3 pixel square to get the level of interaction enrichment. RING1B and H3K27me3 ChIP-seq peaks (Illingworth et al., 2015 (link)) were lifted over to the mm9 mouse genome assembly. The coordinates of biochemically defined CpG islands were taken from (Illingworth et al., 2010 (link)). CTCF ChIP-seq peaks were taken from Bonev et al. (2017) (link) and, following liftOver to the mm9 assembly, intersected with CTCF motifs found in the mm9 genome using Biopython’s motifs module (Cock et al., 2009 (link)). A human CTCF position-frequency matrix was downloaded from JASPAR (MA0139.1). We used only motifs with a score >7 and discounted peaks containing >1 motif.
Regions of high insulation (meaning low number of contacts crossing this regions) in the Bonev et al. Hi-C data were called using cooltools diamond-insulation from 25 kb resolution data and a window size of 1 Mb. The output was filtered to exclude boundaries with strength <0.1 and then pairs of consecutive boundaries were combined to create an annotation of TADs. TADs longer than 1500 kb were excluded due to their likely artefactual nature (based on both visual inspection, and the fact that TAD sizes are reported to be on the order of a few hundred kbp in mammalian cells; Rao et al., 2014 (link)). The same loop annotations for mouse ES cells were used as in our recent publication (McLaughlin et al., 2019 (link)).
All figure panels were created using matplotlib (Hunter, 2007 ) and assembled in Inkscape.
Flyamer I.M., Illingworth R.S, & Bickmore W.A. (2020). Coolpup.py: versatile pile-up analysis of Hi-C data. Bioinformatics, 36(10), 2980-2985.
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Publication 2020
Cells Chromatin Immunoprecipitation Sequencing CpG Islands CTCF protein, human Diamond Embryonic Stem Cells Genome Hemorrhoids Homo sapiens Mammals Mus Nucleus Solitarius RNF2 protein, human Serum Tietz syndrome
3
Multidimensional Scaling Protocol Design
To conduct MDS, a “proximity matrix” is required; that is, a collection of similarity estimates between each pair of items in the stimulus set. For a set composed of k items, (k * (k − 1)) / 2 proximities must be acquired, such that each item is compared to every other at least once. This means that the number of comparisons grows rapidly as a function of the stimulus set size. For a set of 10 items, 45 comparisons must be made. For sets of 20, 40, and 80, the comparisons grow to 190, 780, and 3160 (respectively), and so on. With large set sizes, it becomes impractical to collect a completed matrix from each person, so data may be concatenated across people to form a single, aggregate matrix. Ideally, the complete matrix will be collected from multiple participants (34 ), so that each pair of items can be rated several times (to safeguard against measurement noise). Commonly, the data are organized into a half-triangular (or square-symmetric) matrix, with the names of each of the stimuli ordered across rows and columns. The similarity rating for each pair of items is then placed at the intersection of the appropriate row and column (although some software packages provide an option to compute proximities from raw data). This matrix (or matrices, for multiple participants) is then analysed in statistical software packages, such SPSS (35 ).
Proximity data may take the form of similarities or dissimilarities (e.g., “How alike / different are these two items?”), and can be collected in a variety of ways. Broadly, methods for data collection can be categorized as direct or indirect (see 36 for a review). In direct methods, people knowingly assess the items. The simplest technique is to provide people with two items at a time, and ask them to rate how similar they are to one another, using a Likert scale or a slide-bar. People do this many times – at least once for every pairwise combination of items – and the mean ratings then comprise a proximity matrix for each participant. Alternative direct methods have people categorize items according to some criteria (37 ), or sort them into “piles” based on their similarity (38 ). Proximities are then calculated by counting how often items are sorted into the same category across participants. Higher degrees of similarity are indicated by items being categorized together more often.
In indirect methods, proximities are derived from secondary empirical sources. For instance, stimulus confusions or generalizations are commonly obtained from speeded same-different judgment tasks. Here, proximities can be estimated by identifying the proportion of times that items are mistakenly identified as the same (39 (link)), or by the speed of accurate discriminations (40 ). Higher degrees of similarity are indicated by higher rates of confusion, or slower discrimination times.
Hout M.C., Papesh M.H, & Goldinger S.D. (2012). Multidimensional scaling. Wiley interdisciplinary reviews. Cognitive science, 4(1), 93-103.
Publication 2012
Discrimination, Psychology Generalization, Psychological Hemorrhoids
4
Customizable R Scripts for Bioinformatics Analyses
Although we sincerely believe that the set of quality control measurements and tools that we provide is the most useful, this may be a matter of personal preference. In order to allow for easy addition of custom analysis, we have incorporated R language script editing into the BioWardrobe web interface for both basic and advanced analysis steps. System administrators can add custom R scripts in the R tab, and biologists can run these scripts via the graphical web interface. In the basic analysis, customized R scripts can be run for each sample automatically or for selected samples. As an example, we have added scripts that provide the histogram of read pile-up or island length for ChIP-Seq data or gene body coverage and RPKM histogram for RNA-Seq data (Additional file 1: Figure S5). In the advanced analysis R interface, customized scripts can be provided by system administrators. Users can select records of interest via the graphical user interface and run the customized scripts as needed. As an example, we provide a principal component analysis (PCA) script that can be used for analysis of RNA-Seq data and an IDR2 script that can be used to analyze reproducibility of ChIP-Seq experiments (see [22 ] for output).
Kartashov A.V, & Barski A. (2015). BioWardrobe: an integrated platform for analysis of epigenomics and transcriptomics data. Genome Biology, 16(1), 158.
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Publication 2015
Administrators Chromatin Immunoprecipitation Sequencing Genes Hemorrhoids Human Body RNA-Seq
5
Card Sorting Task for Cognitive Flexibility
Participants (N = 246; ages, 7–89 years; 45.5% female; 10.7% left-handed) completed a card sorting task (Fig. 2a) modeled after Berg (1948) (link) and described more fully elsewhere (Lyvers & Tobias-Webb, 2010 (link)). The instructions were as follows:

You are about to take part in an experiment in which you need to categorize cards based on the pictures appearing on them. To begin, you will see four piles. Each pile has a different number, color, and shape. You will see a series of cards and need to determine which pile each belongs to…. The correct answer depends upon a rule, but you will not know what the rule is. But, we will tell you on each trial whether or not you were correct. Finally, the rule may change during the task, so when it does, you should figure out what the rule is as quickly as possible and change with it. Press any key to begin.

After each trial, feedback of “correct!” or “incorrect” was displayed for 500 ms. The maximum number of trials was 128 (i.e., two decks of 64 cards) but could be shorter (100) on the basis of optimal category completions. The rule (color, shape, or number) could switch as quickly as every tenth trial. The primary dependent measure was the percentage of the total number of trials with perseverative errors. A perseverative error was defined as an incorrect response to a shifted or new category that would have been correct for the immediately preceding category. Response time was also obtained for correct and incorrect decisions for each participant, although excessively short (<100 ms) or long (>10 s) trial times were excluded prior to calculating the mean for each participant.
Piper B.J., Li V., Eiwaz M.A., Kobel Y.V., Benice T.S., Chu A.M., Olsen R.H., Rice D.Z., Gray H.M, & Mueller S.T. (2012). Executive function on the Psychology Experiment Building Language tests. Behavior research methods, 44(1), 110-123.
Publication 2012
Hemorrhoids Woman

Most recents protocols related to «Hemorrhoids»

1
Soil Restoration Enhances Peach Orchard Productivity
A self-invented soil restoration practice-RERP (patent application No. 2021115124384) was employed in the experimental peach orchard (Sun et al., 2022 (link)). RERP can increase the productivity of the peach orchard (reflecting in fruit yield and quality) by improving soil physical, chemical, and microbial properties in the root zone. In order to implement RERP, a trench (12 m long, 0.8 m wide, and 0.6 m deep) that was 1.2 m apart from the west side of the trees were dug out in October 2020. The dug-out soil was placed in three piles and filled back to the trench according to soil depths (0–20 cm, 20–40 cm, and 40–60 cm). There were three treatments that applied on a total of 45 trees, with three replications for each treatment. Border rows were arranged around test trees. Treatment 1 (T1) was RERP with soil conditioner (3 t ha−1), organic fertilizer (15 t ha−1 DW), and mineral fertilizer (N:P2O5:K2O = 15:5:10, 900 kg ha−1) and evenly applied at soil depths of 20, 40, and 60 cm in the trench. Treatment 2 (T2) was RERP with organic fertilizer. Application materials and methods of T2 was the same as T1, only without the application of 3 t ha−1 soil conditioner. Conventional practice (CK) in the orchard was considered as control, receiving 650 kg ha−1 of urea (46%), 600 kg ha−1 of calcium superphosphate, and 310 kg ha−1 potassium sulfate each year (applied on May and November). Pest control and regular management were conducted as required, following the local practices for all treatments.
Sun N., Zhang W., Liao S, & Li H. (2023). Is foliar spectrum predictive of belowground bacterial diversity? A case study in a peach orchard. Frontiers in Microbiology, 14, 1129042.
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Publication 2023
acid calcium phosphate DNA Replication Fruit Hemorrhoids Minerals phosphoric anhydride Physical Examination Plant Roots potassium sulfate Prunus persica Trees Urea
2
Compost-Derived Humic Substance Extraction
Green composts (CO) were produced in Experimental Farm of the University of Naples “Federico II” at Castel Volturno (CE), according to the relevant guidelines and regulations, as reported in Savarese et al., 2022 [2 ]. Briefly, horticultural residues of fennel crop were mixed with coffee husks at 60/38 w/w plus 2% of mature compost as a starter. The vegetable wastes were placed in static piles with bottom-up oxygen fluxes to ensure aerobic transformation. The composting process lasted 100 days, including the thermophilic and mesophilic phases and a final maturation period. To extract humic substances (HS), finely ground compost (100 g) was suspended in 0.1 mol L−1 KOH solution and shaken for 24 h. Then, the extract was centrifuged at 7000 rpm for 20 min and filtered through glass-wool. This extraction was repeated twice (1 h agitation step) and the resulting filtrates were combined. Total extracts, containing both humic and fulvic acids, were acidified to pH 7.4 with 6 mol L−1 HCl and dialysed (1 kD cut-off Spectrapore membranes) against deionized water until the electrical conductivity was lower than 0.5 dS m−1, and freeze-dried for further analysis.
Verrillo M., Cuomo P., Montone A.M., Savy D., Spaccini R., Capparelli R, & Piccolo A. (2023). Humic substances from composted fennel residues control the inflammation induced by Helicobacter pylori infection in AGS cells. PLOS ONE, 18(3), e0281631.
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Publication 2023
Bacteria, Aerobic Coffee Crop, Avian Electric Conductivity Foeniculum vulgare Freezing fulvic acid Hemorrhoids Humic Substances Oxygen Tissue, Membrane Vegetables
3
Colorectal Polyp Risk Assessment
Clinical outcomes were collected for all patients who provided a viable sample, by requesting copies of examination reports from participating sites every month. All diagnoses were determined by reviewing endoscopy, radiology, and histology reports, clinic letters, and urgent referral forms provided by the participating sites. Patient and clinical data included symptoms, reasons for the referral, medical history, and sociodemographic factors. All diagnoses were verified by medical members of the central research team.
All neoplastic bowel polyps, either adenomatous polyps or sessile serrated polyps, were identified and were given a risk of either ‘low’, ‘intermediate’, or ‘high’ depending on their size and frequency; contemporary UK and European guidelines were used in this study23 ,24 (link), with low risk defined as 1–2 adenomas less than 10 mm, intermediate risk as 3–4 small adenomas less than 10 mm or one adenoma 10 mm or more, and high risk as five or more adenomas less than 10 mm or three or more adenomas with at least one 10 mm or more.
Non-neoplastic polyps, such as hyperplastic, inflammatory, or pseudopolyps, were classified separately. Patients with a risk score for their polyps at first, second, or third examinations had their cumulative number and/or highest risk polyp taken as their final score. Remaining bowel pathology was classified as one of CRC, inflammatory bowel disease (colitis/proctitis), diverticulosis, haemorrhoids, normal examination, or procedure stopped/incomplete. Patients with concurrent polyps and CRC were classified as CRC and not included in the analysis, as our target group for this study was those without CRC in whom we could potentially identify polyps and plan for removal before they could progress to CRC.
Bath M.F., Malhi A., Ayling R.M., Seward E., Pritchard-Jones K., Laszlo H.E., Hackshaw A, & Machesney M.R. (2023). Faecal immunochemical testing for haemoglobin in detecting bowel polyps in symptomatic patients: multicentre prospective cohort study. BJS Open, 7(2), zrac161.
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Publication 2023
Adenoma Adenomatous Polyps Colitis Diagnosis Diverticulosis Endoscopy, Gastrointestinal Europeans Hemorrhoids Hyperplasia Inflammation Inflammatory Bowel Diseases Intestinal Polyps Intestines Neoplasms Patients Physical Examination Polyps Proctitis X-Rays, Diagnostic
4
Fiber Supplementation for Bowel Health
Water-insoluble, non-fermentable fibers immediately increase the luminal size, which in turn results in shorter gut transit time, which promotes laxation. On the other hand, water-soluble fibers have a high water-holding capacity, which results in bulky, soft stools that are easier to pass (Slavin, 2013 (link)). Two different meta-analyses indicate that fiber supplementation significantly increases stool frequency compared to placebo (Yang et al., 2012 (link); Christodoulides et al., 2016 (link)). The risk of getting hemorrhoids may be reduced by taking a fiber supplement; if doing so reduces, the symptoms of constipation and the straining that accompany it.
Haque A., Ahmad S., Azad Z.R., Adnan M, & Ashraf S.A. (2023). Incorporating dietary fiber from fruit and vegetable waste in meat products: a systematic approach for sustainable meat processing and improving the functional, nutritional and health attributes. PeerJ, 11, e14977.
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Publication 2023
A Fibers Constipation Dietary Fiber Dietary Supplements Feces Fibrosis Hemorrhoids Laxatives Phenobarbital Placebos
5
AMOC and North Atlantic Climate Indices
AMOC intensity is defined as the maximum overturning streamfunction below 300 m over 30-50° N in the Atlantic. This is also referred to in the text as the northern AMOC. Similarly, the southern AMOC is defined as the maximum overturning circulation below 300 m over 10-34° S in the Atlantic. The “warming hole” SST based index TNA, is defined as annual mean, SPNA mean (15–40°W,46–60°N) SST minus annual mean, global mean SST. This definition is a simplified version of that of ref. 35 (link) but with similar results27 (link) (not shown). The “salinity pile-up” index SS, is defined as annual mean, STSA mean (averaged over 10–34°S) SSS minus annual mean, STSIP mean SSS at the same latitude band. The MMEMs for these two indices are calculated with each ensemble member equally weighted. The AMO index shown in Fig. 1e is calculated after ref. 14 (link) as the weighted mean SST over the North Atlantic (0° N to 60° N) relative to the mean SST from the period 1900-1950 with the global mean SST (60° S to 60° N) removed.
Zhu C., Liu Z., Zhang S, & Wu L. (2023). Likely accelerated weakening of Atlantic overturning circulation emerges in optimal salinity fingerprint. Nature Communications, 14, 1245.
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Publication 2023
Hemorrhoids Salinity

Top products related to «Hemorrhoids»

1
Dumont 5 forceps by Electron Microscopy Sciences
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More about "Hemorrhoids"

Hemorrhoids, also known as piles, are a common anorectal condition characterized by the swelling and inflammation of veins in the lower rectum and anus.
This painful condition can cause a range of unpleasant symptoms, including itching, bleeding, and discomfort.
Effective treatment options for hemorrhoids may involve a combination of self-care measures, such as using Dumont #5 forceps to gently treat the affected area, and medical interventions, such as the use of 96-well plates and N-phenylthiourea to assess the efficacy of potential therapies.
Researchers and clinicians can leverage powerful tools like the Autokit Glucose reagent and FluoroCube fluorimeter to analyze and optimize hemorrhoid treatments.
By utilizing advanced technologies like the NextSeq 500 and SAS statistical software, they can achieve greater reproducibility and identify the most effective hemorrhoid products, including Isoproterenol and One Shot TOP10 Electrocomp E. coli.
Additionally, understanding the role of porcine kidney trehalase in the pathophysiology of hemorrhoids may provide valuable insights for developing targeted interventions.
PubCompare.ai's AI-driven protocol comparison tool empowers researchers to effortlessly locate and analyze the best treatment protocols from literature, preprints, and patents, helping them optimize their hemorrhoids research and stay at the forefront of evidence-based medical advancements.
Experience the future of hemorrhoid research today with this powerful platform.