The value of DNA image cytometry combined with brush routine cytology in diagnosing indeterminate biliary strictures: A large sample size retrospective study

2019 ◽  
Vol 34 (11) ◽  
pp. 2036-2042 ◽  
Author(s):  
Xia Yang ◽  
Liqi Sun ◽  
Jiefang Guo ◽  
Li Gao ◽  
Chengyong Qin ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Sample Size ◽  
Image Cytometry ◽  
Large Sample Size ◽  
Biliary Strictures ◽  
Large Sample ◽  
Dna Image Cytometry

Heart Rate Response to Exercise

2018 ◽  
pp. 437-445
Author(s):  
Gregory S. Thomas
Keyword(s):  
Heart Rate ◽  
Physical Fitness ◽  
Sample Size ◽  
Older Persons ◽  
Heart Rate Response ◽  
Large Sample Size ◽  
Maximum Heart Rate ◽  
Large Sample ◽  
Response To Exercise

The chapter Heart Rate Response to Exercise reviews the studies performed to estimate a patient’s maximum predicted heart rate. While the commonly used formula (220 – age), developed in 1971, is easy to remember, it underestimates the actual maximum heart rate in older persons. Studies of large sample size have found the maximum heart rate to be relatively independent of sex and physical fitness but to incrementally decline with age. The decrease with age is less than 1 beat per minute per year, however. A more accurate and recommended formula is [(208) – (0.7)(age)] as developed by Tanaka and colleagues.

Extended criterion for comparison of empirical distributions

1970 ◽  
Vol 7 (01) ◽  
pp. 1-20 ◽  
Author(s):  
Ora Engleberg Percus ◽  
Jerome K. Percus
Keyword(s):  
Sample Size ◽  
Generating Function ◽  
Relative Number ◽  
Function Technique ◽  
Large Sample Size ◽  
Large Sample ◽  
Empirical Distributions ◽  
Asymptotic Problem ◽  
Explicit Expressions

A generating function technique is used to determine the probability that the deviation between two empirical distributions drawn from the same population lies within a given band a specified number of times. We also treat the asymptotic problem of very large sample size, and obtain explicit expressions when the relative number of failures is very small or very large.

scholarly journals A Mass-Ratiometry-Based CD45 Barcoding Method for Mass Cytometry Detection

2019 ◽  
Vol 24 (4) ◽  
pp. 408-419
Author(s):  
Hongu Meng ◽  
Antony Warden ◽  
Lulu Zhang ◽  
Ting Zhang ◽  
Yiyang Li ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Sample Size ◽  
Single Cell ◽  
Large Sample Size ◽  
Single Cell Level ◽  
Mass Cytometry ◽  
Cell Level ◽  
Large Sample ◽  
Antibody Staining ◽  
Instrument Sensitivity

Mass cytometry (CyTOF) is a critical cell profiling tool in acquiring multiparameter proteome data at the single-cell level. A major challenge in CyTOF analysis is sample-to-sample variance arising from the pipetting process, staining variation, and instrument sensitivity. To reduce such variations, cell barcoding strategies that enable the combination of individual samples prior to antibody staining and data acquisition on CyTOF are often utilized. The most prevalent barcoding strategy is based on a binary scheme that cross-examines the existence or nonexistence of certain mass signals; however, it is limited by low barcoding efficiency and high cost, especially for large sample size. Herein, we present a novel barcoding method for CyTOF application based on mass ratiometry. Different mass tags with specific fixed ratios are used to label CD45 antibody to achieve sample barcoding. The presented method exponentially increases the number of possible barcoded samples with the same amount of mass tags compared with conventional methods. It also reduces the overall time for the labeling process to 40 min and avoids the need for expensive commercial barcoding buffer reagents. Moreover, unlike the conventional barcoding process, this strategy does not pre-permeabilize cells before the barcoding procedure, which offers additional benefits in preserving surface biomarker signals.

MoFe‐Codoped Ni 3 S 2 /Ni(OH) 2 Nanosheets with Large Sample Size toward High‐Performance Oxygen Evolution

2019 ◽  
Vol 7 (9) ◽  
pp. 1801053
Author(s):  
Liu Xie ◽  
Rui Tong ◽  
Wen Zhang ◽  
Dejian Wang ◽  
Tao Liu ◽  
...  
Keyword(s):  
Sample Size ◽  
Oxygen Evolution ◽  
High Performance ◽  
Large Sample Size ◽  
Large Sample

Application of Big data scientific research analysis platform in clinical medical research

2021 ◽  
pp. 1-12
Author(s):  
Jing Wang ◽  
Jie Wei ◽  
Long Li ◽  
Lijian Zhang
Keyword(s):  
Big Data ◽  
Drug Discovery ◽  
Data Quality ◽  
Sample Size ◽  
Real World ◽  
Medical Data ◽  
Large Sample Size ◽  
Research Implementation ◽  
Large Sample ◽  
Application Systems

With the rapid development of evidence-based medicine, translational medicine, and pharmacoeconomics in China, as well as the country’s strong commitment to clinical research, the demand for physicians’ research continues to increase. In recent years, real-world studies are attracting more and more attention in the field of health care, as a method of post-marketing re-evaluation of drugs, RWS can better reflect the effects of drugs in real clinical settings. In the past, it was difficult to ensure data quality and efficiency of research implementation because of the large sample size required and the large amount of medical data involved. However, due to the large sample size required and the large amount of medical data involved, it is not only time-consuming and labor-intensive, but also prone to human error, making it difficult to ensure data quality and efficiency of research implementation. This paper analyzes and summarizes the existing application systems of big data analytics platforms, and concludes that big data research analytics platforms using natural language processing, machine learning and other artificial intelligence technologies can help RWS to quickly complete the collection, integration, processing, statistics and analysis of large amounts of medical data, and deeply mine the intrinsic value of the data, real-world research in new drug development, drug discovery, drug discovery, drug discovery, and drug discovery. It has a broad application prospect for multi-level and multi-angle needs such as economics, medical insurance cost control, indications/contraindications evaluation, and clinical guidance.

Standard Error of Linear Equating for the Counterbalanced Design

1995 ◽  
Vol 20 (4) ◽  
pp. 337-348 ◽  
Author(s):  
Lingjia Zeng ◽  
Ronald T. Cope
Keyword(s):  
Sample Size ◽  
Computer Simulations ◽  
Standard Error ◽  
Delta Method ◽  
Standard Errors ◽  
Large Sample Size ◽  
Symmetric Distributions ◽  
Large Sample ◽  
Normality Assumption ◽  
Linear Equating

Large-sample standard errors of linear equating for the counterbalanced design are derived using the general delta method. Computer simulations were conducted to compare the standard errors derived by Lord under the normality assumption with those derived in this article without such an assumption. The standard errors derived without the normality assumption were found to be more accurate than those derived with the normality assumption in an example using large sample size and moderately skewed score distributions. In an example using nearly symmetric distributions, the standard errors computed with the normality assumption were found to be at least as accurate as those derived without the normality assumption.

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