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A 113

A 113 is a protein involved in the regulation of cell growth and differentiation.
It plays a role in various cellular processes, including signal transduction, gene expression, and apoptosis.
Research on A 113 is of interest for understanding its potential implications in human health and disease, such as cancer, neurodegeneration, and metabolic disorders.
PubCompare.ai can assist researchers in optimizing their A 113 studies by identifying the most effective protocols from literature, pre-prints, and patents using advanced AI-driven comparisons.
Leverage the power of PubCompare.ai to take your A 113 research to the next lvevel and uncover the most promising solutions.

Most cited protocols related to «A 113»

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Publication 2010
A 113 Allodynia Complex Regional Pain Syndromes Diabetes Mellitus Diabetic Neuropathies Fracture, Bone Hyperalgesia Injuries Injuries, Crush Intervertebral Disk Displacement Lower Extremity Nervousness Neuralgia Operative Surgical Procedures Pain Disorder Patients Peripheral Nerve Injuries Peripheral Nerves Peripheral Nervous System Diseases Physical Examination Plant Roots Radiculopathy Rehabilitation Tarsal Tunnel Syndrome Wrist Joint
Details of study selection, data collection, and harmonization procedures in the Emerging Risk Factors Collaboration (ERFC) have been described previously.19 (link) Studies were identified through electronic searches of databases, scanning of the reference lists of relevant articles (including previously published reviews), and discussion with collaborators of the ERFC (FIGURE 1). Electronic searches, not limited to the English language, were performed in MEDLINE and EMBASE for studies published between January 1970 and March 2009 using terms related to Lp(a) (eg, lipoprotein[a], Lp[a], apo[a], apolipoprotein[a]) and cardiovascular disease outcomes (eg, cardiovascular disease, coronary heart disease, myocardial infarction, stroke).
Studies were considered for inclusion if they had baseline information on age, sex, Lp(a), and several conventional vascular risk factors; if they did not select participants on the basis of having previous cardiovascular disease; used quantitative Lp(a) assay methods; recorded cause-specific mortality and/or major vascular morbidity using accepted criteria; and had accrued more than 1 year of follow-up.
Thirty-six eligible prospective studies,10 (link),15 (link),16 (link),20 (link)-52 (link) including 12 that had not previously published their findings (references 21 (link), 24 (link), 29 (link), 31 (link), 32 (link), 38 (link)-42 (link), 47 (link), 50 (link)), were included. These studies involved a total of 126 634 individuals who had no known prior history of CHD (ie, myocardial infarction [MI] or angina, which was defined in each study) or stroke at the initial (baseline) examination. The contributing studies comprise about 90% of relevant incident CHD cases identified in known Western studies (TABLE 1); several smaller studies (collectively comprising about 10% of relevant known incident CHD cases) could not supply data.53 (link)-61 (link) A few studies62 (link)-64 (link) could not be included because they did not use quantitative assay methods.
Concomitant information was available on Lp(a), age, sex, systolic blood pressure, smoking habits, history of diabetes, body mass index, triglycerides, and total cholesterol in 106 645 participants from 30 studies. A total of 96 113 participants from 26 studies had concomitant data on all the preceding characteristics plus high-density lipoprotein (HDL) cholesterol. To measure Lp(a), 2 studies used in-house assays, 32 used commercially available assays, and 2 did not specify the assay used. Twenty-one studies used enzyme-linked immunosorbent assay methods, 9 immunoturbimetry or nephelometry, 3 immunoradiometry, and 1enzyme immunodiffusion (eTable 1; available at http://www.jama.com). Twenty-four studies used assays insensitive to apo(a) isoforms.
In registering fatal outcomes, all contributing studies used International Classification of Diseases coding to at least 3 digits and ascertainment was based on death certificates. Twenty-eight of the 36 contributing studies also involved medical records, autopsy findings, and other supplementary sources to help classify deaths (eTable 2). Twenty-nine studies used standard definitions of MI based on Monitoring Trends and Determinants in Cardiovascular Disease (MONICA) or World Health Organization criteria. Twenty-five studies reported diagnosis of strokes on the basis of typical clinical features and characteristic changes on brain imaging, and most attempted to provide attribution of stroke subtype.
Publication 2009
A 113 Angina Pectoris Apolipoproteins A Autopsy Biological Assay Blood Vessel Brain Cardiovascular Diseases Cerebrovascular Accident Cholesterol Diabetes Mellitus Diagnosis Enzyme-Linked Immunosorbent Assay Fatal Outcome Fingers Heart Disease, Coronary High Density Lipoprotein Cholesterol Index, Body Mass Myocardial Infarction Nephelometry Protein Isoforms Systolic Pressure Test, Gel Diffusion Triglycerides

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Publication 2017
A 113 Exome Frameshift Mutation Gene Expression Genes INDEL Mutation Malignant Neoplasms Mutation Mutation, Nonsense Neoplasms Patients Recurrence TP53 protein, human Transcription, Genetic
Participants completed three screening forms: the WERCAP Screen, the Stress Screen (22 (link)), and the Substance Screen. These forms are available for public use (http://werc.wustl.edu/home/screeninginstruments).
The WERCAP Screen assesses risk of BPD and psychosis based on a quantification of lifetime symptom burden. The WERCAP Screen consists of a total of 16 questions, the first half of which are designed to assess affective symptoms experienced by individuals, while the remaining questions assess risk of psychosis. The majority of questions (10 out of 16) require two responses, including a rating of the frequency of symptom occurrence, and if present, the severity of the associated functional impairment. For the item probing into decreased need for sleep, respondents were asked to rate symptom duration rather than degree of functional impairment. Six questions in the affectivity section assess only symptom frequency, since they inquire about symptoms that do not typically impair function, or the degree of resulting dysfunction is difficult to assess. The responses were converted into numerical values as follows: No = 0, Once = 1, Rarely (yearly–monthly) = 3, Often (>monthly–weekly) = 4, and Almost Always (>weekly–daily) = 5. For items assessing effect on functionality, responses were converted as follows: Not at All = 0, A Little = 1, Moderately = 2, Severely = 3. The frequency and functionality scores for items 1–8 were summed to generate a composite aWERCAP (affectivity) score, and the remaining items (9–16) were summed to generate the composite pWERCAP (psychosis) score. The maximum aWERCAP score possible is 49, and the maximum pWERCAP score is 64, yielding a maximum total WERCAP Screen score of 113.
The Stress Screen assesses the total stress burden of 23 common psychosocial stressors, such as one’s relationships with family and friends, substance use, or the workplace [see Ref. (22 (link)) for a complete list]. Space is also provided to write in up to two additional stressors. By default, the Stress Screen is designed to capture the current stress load on an individual. Respondents are asked to rate the extent to which they are affected by each stressor by marking the appropriate checkbox. Each response is converted to a numerical rating (No = 0, A Little = 1, Moderate = 2, A Lot = 5, Severely = 10), and then summed to generate the Stress Screen score. The maximum Stress Screen score possible is 230.
The Substance Screen is a 20-item questionnaire that assesses the current substance-use habits of the respondent. Respondents were asked to rate their usage frequency of a variety of psychotropic substances, such as caffeine, nicotine, prescription drugs, etc. [see Ref. (22 (link)) for a complete list] and could also choose to write in up to two additional substances. The frequency responses were converted into a numerical score using the same scale as that in the WERCAP Screen, and then summed to generate the Substance Screen score.
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Publication 2016
A 113 Affective Symptoms Caffeine Friend Nicotine Prescription Drugs Psychotic Disorders Psychotropic Drugs Sleep Substance Use

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Publication 2015
A 113 Enhanced S-Cone Syndrome Genes Human Body negative elongation factor B, human RNA RNA-Seq

Most recents protocols related to «A 113»

Example 4

In total, 23,400 lines were screened from crosses segregating for Sm1 with ten SNP markers distributed across the Sm1 locus in order to search for extra recombinants within this region. In total, 576 putative recombinants were identified and these, plus their parental lines, were also genotyped with two Real-Time PCR markers developed from RGA 1 and RGA 2 (Table 1B) and 46 markers that included 24 SNPs from within the interval and 22 markers tightly flanking the region. The results showed that no recombination events were found between the two RGA genes and no recombinant plants were found within the small 0.067 cM region identified in the Xi19 x Robigus bi-parental mapping population. The lack of recombination within the region is due to the absence of any sequence homology between resistant and susceptible lines. Moreover, all the lines that carried the two Robigus RGA genes shared the Robigus haplotype based on the 24 markers within the target interval suggesting a single origin and a common ancestor for the Sm1 resistance locus.

Amongst the 576 lines, a sub-panel of 113 diverse lines was selected for phenotypic analysis. This sub-panel contained many recombinant plants arising from different genetic origins in order to validate any potential diagnostic SNP markers. All the recombinant plants and their parental lines were sown and genotyped in summer 2014. The presence of the two RGA genes was always shown to be 100% diagnostic for the presence of Sm1. From these 24 markers within the interval, five were found to be correlated with the presence and absence of the two RGAs (Table 1A), which makes them ideal for marker-assisted selection of the Sm1 gene.

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Patent 2024
A 113 Diagnosis Genes Genetic Markers Haplotypes Parent Phenotype Plants Real-Time Polymerase Chain Reaction Recombination, Genetic

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Publication 2023
A 113 Compulsive Behavior Medical Internship Nurses Operative Surgical Procedures Relaxation Therapy Student Student Selection
The Tohoku Medical Megabank (TMM) was administered by the Tohoku Medical Megabank Organization (ToMMo), established at Tohoku University in Miyagi Prefecture, and by the Iwate Tohoku Medical Megabank Organization, established at Iwate Medical University in Iwate Prefecture, to restore medical services and the health of residents after the Great East Japan Earthquake, which spawned a devastating tsunami that damaged vast areas of the northeastern coast of Japan on March 11, 2011.13, 14, 15 Details regarding the TMM Project were reported previously.15This was a cross‐sectional study that screened 66 874 Japanese community residents who underwent a voluntary health checkup conducted by the ToMMo between January 2012 and December 2015 in Miyagi and Iwate Prefectures, Japan. Among participants, 54.070 reported a confirmed medical history of hypertension according to a self‐administered questionnaire. A total of 17 115 study participants were receiving treatment for hypertension, and two participants were excluded due to missing SUA concentration data. Thus, a total of 17 113 eligible participants were included in the present study (6499 men; 10 614 women).
Publication 2023
A 113 Earthquakes High Blood Pressures Japanese Tsunamis Woman
A total of 327 children (152 boys and 175 girls) from all the DCCs were included in this study. The DCCs are five governmentally initiated centers that offer specialized support and in collaboration with municipalities coordinate cases of suspected child abuse. When two or more public sectors (e.g., municipality, police, or hospital) are involved in a case of suspected child abuse, the local DCC are mandated to coordinate the cross-professional work on the case. The DCCs are placed in each of the five regions of Denmark. The DCCs are child-centered which means that the child goes to the DCC and all professionals such as caseworkers, police, and prosecutors come to the DCC to work on the case. Also, by law the centers have to be child friendly in their interiors. Furthermore, the DCCs offers a specialized trauma-focused psychosocial screening of the child’s need for support and treatment no matter the turn-out of the legal case.
A team of one psychologist and one social worker conducts the screening of the child. The screening is conducted during up to 4 meetings with the child and a caregiver. All DCCs use a national assessment battery which include standardized and validated assessment tools for children aged 1-17 years. It is up to the professionals, who conduct the screening, which measure(s) to use depending on the case content and the child’s age, cognitive skills, and willingness to participate and disclose symptoms and mental state (25 ) . The Darryl cartoon test is part of the national assessment battery and available in two versions in the DCCs: one for use after exposure to physical abuse and one for use after exposure to sexual abuse. In the DCCs, the Darryl test is used in cases where the children to some extent are able to talk about the abuse on which they have been referred to the DCCs.
Data were collected between May 2018 and December 2020. All included children answered a Darryl cartoon test. A subsample of 113 children also filled out the BYI-II and caregivers for 63 children filled out the SDQ caregiver version. The study was approved by RIO, the Data Protection Agency of the University of Southern Denmark.
The children were between the ages of 6 to 17 years. Mean age of was 9.9 years (SD = 2.18). The distribution by age groups was: 146 children were between 6 and 9 years, 144 children were between 10 and 12 years and 37 children were between 13 and 17 years of age.
Publication 2023
A 113 Abuse, Child Abuse, Physical Age Groups Boys Child Cognition DCC protein, human Drug Abuse Psychologist Public Sector Sexual Abuse Speech Woman Worker, Social Wounds and Injuries
DIABLO [53 (link)] stands for Data Integration Analysis for Biomarker discovery using Latent cOmponents and performs supervised multi-omics data integration, by maximizing the correlation between co-expressed elements in the input datasets. DIABLO algorithm extends sparce Generalized Canonical Correlation Analysis [54 ] and by expanding the Partial Least Squares (PLS) regression, used singular value decomposition for dimensionality reduction and selected co-expressed (correlated) variables that could explain the categorical outcome of interest, in our case the five SOM-derived metabotypes. DIABLO analysis was conducted in R (version 3.6.3) and RStudio (version 1.2.5033) through the package of mixOmics [55 (link)] (version6.10.9). DIABLO output a set of latent variables (components) based on the dimensionality and the importance of the input datasets. All the datasets in this study carried the same weight, hence the DIABLO dataset matrix initialization design parameter was diagonal. The original input was 289 metabolites, 119 microbial species and 776 genes, all the differentially identified components from the omics datasets. This chosen number of components could extract sufficient information to discriminate all SOM-defined metabotypes. Then, a set of coefficients was attributed to each variable, that indicated the importance of each variable in DIABLOMulti-omics Datasets. The goal was to have maximization of the covariance between a linear combination of the variables from each input dataset and each categorical outcome. The algorithm was optimized with a 10-fold validation over 10 training epochs. After tuning these two hyperparameters (number of variables from each dataset, choice of variables that maximize co-variance), DIABLO produced as output a minimal signature of total 113 markers that distinguish the given metabotypes.
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Publication 2023
A 113 Biological Markers EPOCH protocol Genes Maritally Unattached

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More about "A 113"

Protein Regulation in Cell Growth and Differentiation: Exploring A 113's Role A 113 is a critical protein involved in the regulation of cellular processes, including growth, differentiation, signal transduction, gene expression, and apoptosis.
This protein has garnered significant research interest due to its potential implications in human health and disease, such as cancer, neurodegeneration, and metabolic disorders.
Researchers leveraging advanced tools like PubCompare.ai can optimize their A 113 studies by identifying the most effective protocols from the vast body of literature, pre-prints, and patents.
This AI-driven comparison platform empowers scientists to uncover the most promising solutions for their A 113 research, taking their work to new heights.
Beyond A 113, researchers may also explore related proteins and cellular mechanisms using a variety of analytical techniques and technologies.
For instance, the Neuroscan Acquire system and Neuroscan 128-channel Quik-Cap can be utilized for electroencephalography (EEG) studies, while the DNeasy Blood and Tissue Kit facilitates DNA extraction for genetic analyses.
High-throughput sequencing platforms, such as the HiSeq 4000, HiSeq 2000, HiSeq 2500, and NextSeq 550, enable researchers to delve deeper into the genomic and transcriptomic landscapes surrounding A 113 and its interactions.
Additionally, the Phage DNA Isolation Kit and the ABI 7500 FAST sequence detector system can support investigations into the role of viral elements and gene expression, respectively.
By leveraging these advanced tools and techniques, researchers can gain a more comprehensive understanding of the complex regulatory networks and pathways involving A 113, ultimately paving the way for groundbreaking discoveries and advancements in the field of cellular biology and human health.