CSV Files Research Articles

Smartwatch: Looking beyond what you see

BackgroundThe clinical utility of the Apple Inc.® smartwatch in scenarios beyond detecting atrial fibrillation has been debated. Although the device has the capability to record electrocardiograms (ECG) and detect arrhythmias, voltage limitations hinder its accuracy in measuring real voltage when recording precordial leads. This limitation poses challenges for its clinical use in diagnosing ischemia and screening cardiomyopathies. This review aims to analyze the ECG recording capacity of the Apple Watch, investigate the reasons for voltage limitations, and explore alternative approaches for its use in these clinical scenarios. MethodsA comprehensive literature review was conducted to examine the ECG recording capacity of the Apple Watch and the limitations encountered when recording precordial leads. Data in CSV format files were analyzed to gain insights into the underlying causes of voltage limitations. ResultsThe Apple Watch demonstrates effectiveness in detecting cardiac arrhythmias such as atrial fibrillation using photoplethysmography and ECG recording. However, voltage limitations during precordial lead recordings impede accurate voltage measurement, thereby limiting its clinical utility. Analysis of the data stored in the CSV files revealed that these voltage limitations are primarily attributed to the presentation format. Exploring alternative approaches for data processing could potentially overcome this challenge. ConclusionsThis review highlights the potential for addressing voltage limitations through alternative data processing approaches. Further research is necessary to identify suitable alternatives that enable the Apple Watch to be effectively utilized in these clinical scenarios.

Accounting Ethics, Financial Reporting Focused on Financial Frauds: A Bibliometric Analysis

Purpose: The aim of this research is to carry out a bibliometric analysis of accounting ethics and its relationship with financial reports and financial fraud Theoretical framework: Accounting ethics refers to the set of principles and values that govern the professional conduct of accountants in their daily work activity. Likewise, frauds on financial statements have generated accounting scandals and they include unethical behavior by public accountants. Understanding and analyzing the relationship between accounting ethics, reports, financial fraud and their evolution over time is essential for improving the curriculum of the accounting student. Design/Methodology/Approach: Data from the SCOPUS database was collected from 1986 to 2022. Descriptive and inferential statistical methods were used to carry out a bibliometric analysis wich included, articles, reviews and conference reviews. In order to analyse research trends, no type of documents were excluded. The citation and bibliographic information, as well as the abstract and keywords of the obtained results were exported to a CSV file. Furthremore, the bibliometric analysis was performed using R studio software through the biblioshiny platform. Findings: The findings revealed that the field of accounting ethics in relation to financial reporting or financial fraud is not well established in the scientific literature, but has potential for growth in the near future. The results reported that since the year 2000 this area has had an exponential growth led by the countries of the US, Australia and Spain that contributed documents in this area compared to other countries. The new developments in the field of accounting ethics have not been sufficiently studied and understood in a logical and concise manner. This study used a conceptual framework to organize and analyze the various streams and research topics in the field. These themes and subthemes have suggested research recommendations and crucial research areas. Research, Practical & Social implications: The authors recommend more research in the field of accounting ethics with other accounting areas and thus identify areas that need further exploration. The current study can help researchers and recruiters analyze the upcoming trends in accounting ethics by helping decision-makers in accountant education. Originality/Value: The research turns out to be an original investigation that contributes to the bibliometric representation in the field of accounting ethics.

Study on Software Defect Prediction Based on SVM and Decision Tree Algorithm

Software Defect Prediction is a process of identifying the potential defects in software systems before they occur. In this approach, the dataset containing information about software attributes is used as input, and the output is the prediction of whether the software is defective or not. The input dataset is generally in the form of a CSV file, which contains various software attributes such as cyclomatic complexity, essential complexity, Design Complexity, etc. The output of the defect prediction is binary classification. It is done by using SVM (Support Vector Machine) and a decision tree algorithm. This approach can help software developers identify their systems' defects before they cause any harm or affect the system’s performance.

Online maintenance of evolving knowledge graphs with RDFS-based saturation and why-provenance support

Enterprise RDF knowledge graphs are often built using extraction data pipelines that are fed by several heterogeneous sources (relational databases, CSV files or even unstructured textual data). As a direct consequence, the construction of these KGs undergoes a number of changes in the early stages of their life cycle, which are initiated by a human developer and therefore need to be done interactively and efficiently. Driven by such needs, in this paper, we present a solution for the incremental maintenance of KGs given user-prescribed changes. A key feature of the proposed solution is the support of provenance collection that can be used to assist the developer in the analysis and debugging of the KG. Specifically, we strive to compute and maintain the provenance of asserted and inferred facts in the knowledge graph incrementally (and thus efficiently). The evaluation exercises we have conducted show the effectiveness of our solution and highlight the parameters that impact performance.

PyGellermann: a Python tool to generate pseudorandom series for human and non-human animal behavioural experiments

ObjectiveResearchers in animal cognition, psychophysics, and experimental psychology need to randomise the presentation order of trials in experimental sessions. In many paradigms, for each trial, one of two responses can be correct, and the trials need to be ordered such that the participant’s responses are a fair assessment of their performance. Specifically, in some cases, especially for low numbers of trials, randomised trial orders need to be excluded if they contain simple patterns which a participant could accidentally match and so succeed at the task without learning.ResultsWe present and distribute a simple Python software package and tool to produce pseudorandom sequences following the Gellermann series. This series has been proposed to pre-empt simple heuristics and avoid inflated performance rates via false positive responses. Our tool allows users to choose the sequence length and outputs a .csv file with newly and randomly generated sequences. This allows behavioural researchers to produce, in a few seconds, a pseudorandom sequence for their specific experiment. PyGellermann is available at https://github.com/YannickJadoul/PyGellermann.

Article Processing Charges in Gold Open Access Journals: An Empirical Study

This study focuses on analyzing the trends in article processing charges (APCs) levied by open access journals. To gather the required data, a CSV file was generated from the Directory of Open Access Journals (DOAJ). The APC values were assessed and converted into standardized currencies, including INR and USD. Among the 17,379 journals included in the DOAJ, only 5,122 journals were found to charge APCs. Through the examination of the collected data, it was discovered that the highest APC amount recorded was INR 518,334.95 (equivalent to USD 6680.46), while the lowest APC observed was INR 1.04 (equivalent to USD 0.013).

Machine Learning to Identify Monkey Pox Disease

In May 2022, it has received by WHO reports from non-endemic countries on cases of monkey pox disease. Monkey pox is a rare zoonotic disease caused by infection with the monkeypox virus that belongs to the genus orthopoxvirus and the family poxviridae, and also the variola virus. This study aims to classify patients who have contracted the monkey pox virus. We modeled an analysis of monkey pox disease and conducted comparisons utilizing a dataset from Kaggle consisting of a CSV file with records for 25,000 patients. The monkey pox dataset was analyzed using the correlation coefficient and the number of target variables. Machine learning (ML) methods are used for classification by utilizing the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB) algorithms. This study resulted in the highest classifier Gradient Boosting (GB) algorithm with an accuracy value of 71%. then the accuracy obtained by Support Vector Machine (SVM) is 69%, Random Forest (RF) accuracy is 68%, and finally K-Nearest Neighbor (KNN) obtains 63% accuracy. This ML method is expected to analyze monkey pox disease so that it helps the country and government, especially the health field in assessing, identifying, and being able to take appropriate action against monkey pox disease.

PAT: Phytosociology Analysis Tool

Researchers quantify vegetation characteristics using phytosociology studies. The data collected from vegetation surveys, such as tree girth and height measurements, are used to calculate various phytosociological attributes like frequency, density, basal cover, important value index, etc. In general, they are calculated using an Excel spreadsheet and a lengthy mathematical process. There are a few open-source or free software available to compute some of these parameters. However, they are not user friendly for most researchers. Also, these software primarily doesn’t compute basic phytosociological attributes. In view of these constraints and to ease researchers work, we have developed a simple tool, “PAT” Phytosociology Analysis Tool,” that computes the maximum attributes from the field collected vegetation data. The PAT GUI is simple. PAT computes results using standard formulas after users upload a csv file in the required format. Users can download the table's results and figures in PNG format.

Automatic Meter Detection and Recognition: A Robust Approach Based on Image Processing Techniques

Abstract: To address the issue of manual reading, Automatic Meter Reading (AMR) is introduced, which is a combination of detection and recognition that is efficiently applied to identify the meter and accurately read the digits on electric meters. The initial step in meter detection is the collection of the dataset, after which the annotation process generates XML files that are converted to CSV files and the label map is generated. Using a pre-trained classifier SSD ResNet, the model is trained and evaluated. The identified photos are used as input meter images in meter recognition, where a sequence of pre-processing techniques is performed. After which, using tesseract and leptonica, a model is generated from source, and digit recognition is conducted. The accuracy of 95.45% is then generated after training and evaluating on 2000 images of different kinds of electric meters in various surroundings.

Digital diagnostics: A computer application for lymph node metastases in cervical cancer

BACKGROUND: Since 2020, staging accuracy improvements in cervical cancer (CC) revealed that major errors occur due to missed metastases to regional lymph nodes. The presence or absence of metastases seriously affects the staging of cancer and, consequently, its treatment methods, since metastases make cancer inoperable.
 AIM: To develop an application to help the doctor in predicting metastases to regional lymph nodes in CC, with the ability of installing on any personal computer regardless of the operating system (Windows, Linux, or Mac OS) and saving data about the patients.
 METHODS: The development was performed on the .NET platform (framework v4.7.2), by means of C# language, based on a proprietary prognostic model using seven blood parameters such as erythrocyte sedimentation rate, erythrocytes, hemoglobin, fibrinogen, D-dimer, and platelet aggregation with adenosine diphosphate and soluble fibrin monomer complexes.
 RESULTS: A window application was developed that automatically calculates the probability of metastases to regional lymph nodes in СС and provides the ability to save the data from the input form and the predicted value into TXT and/or CSV files. TXT and CSV formats are supported in various operating systems. The .txt file allows each individual patients prognosis results to be saved in a separate file for easy printing and attachment to the patients electronic or paper medical record. The CSV file format enables the aggregation of data of all patients by adding line-by-line data from the input form to the end of the file.
 CONCLUSIONS: The developed application facilitates the process of using the patented formula to diagnose metastases to regional lymph nodes in СС at the early stages of examination, which allows choosing the optimal tactics and, consequently, improving the prognosis of the disease.

Laboratory Tests of the Resistance of an Unmanned Aerial Vehicle to the Normalized near Lightning Electrical Component

This article describes a method of immunity testing for commercial unmanned aircraft vehicles relative to a variable near-lightning electric field component. The research focuses on one of the components of the electromagnetic field generated during a lightning discharge: the electrical component. Studies are proposed showing the influence of only this one factor on overvoltages arising in the drone. So far, no one has analyzed such an impact from emerging disturbances, because previous studies in the area have largely considered the impact of the entire electromagnetic field. This is justified practically, but not scientifically—it is necessary to determine the impact of each component separately. Selected electronic components are tested here. For that purpose, two types of pulses are used: Wave Form 4 (WF4–6.4/69 μs from DO-160 standard) and Voltage waveform 1.2/50 μs (PN-EN 61000-4-5:2014-10). The testing object is centrally placed in a capacitor between two parallel plates of dimensions 2 m by 2 m to provide a homogeneous electric field. The results (from a Rigol 1054Z oscilloscope) are saved in *.CSV files (for further analysis). The research shows that the greatest overvoltages are in active parts of the drone (higher-than-supply voltage level), such as the RF antenna or semiconductors. This emphasizes the need to pay special attention to the protection of these elements against a pulsed electromagnetic field, especially the electric component (e.g., lightning discharge).

1118-P: Accuracy of Dexcom G6 Continuous Glucose Monitor in Pediatric Diabetic Ketoacidosis Admissions

Continuous glucose monitors (CGMs) are an integral part of care for youth with type 1 diabetes (T1D) though lack FDA labeling for inpatient use. While some adult data on CGM use in inpatient settings is available, pediatric data are minimal. This retrospective chart review evaluated the accuracy of Dexcom G6 CGM versus point of care (POC, Nova Biomedical StatStrip [MARD 6%])) blood glucose values from pediatric inpatient encounters. Blood glucose data, diagnosis codes, and initial labs were collected from the medical record. CGM values were obtained from Dexcom Clarity CSV files. Paired glucose values (N=1191) from 83 patients with T1D (median age 12 yrs, 54% male, 69% non-Hispanic White) were used to calculate mean absolute relative difference (MARD) and Clarke Error Grid. Data from DKA admissions (N=665) had a MARD of 11.1% with 97.8% of values within A&B zones, compared to 11.4% and 98.5% for non-DKA admissions (N=526). Values from severe DKA admissions (N= 307) (pH <7.15 and/or bicarbonate <5 mmol/L) had a lower MARD compared to non-severe admissions (N=358) (8.4% vs 13.4%, p=0.01). In summary, CGM accuracy is comparable between DKA and non-DKA admissions. The accuracy of CGMs, even in severe DKA, suggests potential usability during pediatric hospital encounters. Further analysis will differentiate POC versus lab glucose and the effect of medications, including IV insulin infusions. Disclosure L.A.Waterman: None. L.Pyle: None. L.Towers: None. E.Jost: Other Relationship; Tandem Diabetes Care, Inc. A.J.Karami: None. C.Berget: Consultant; Insulet Corporation, Dexcom, Inc., Other Relationship; Tandem Diabetes Care, Inc. G.P.Forlenza: Advisory Panel; Medtronic, Consultant; Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care, Inc., Lilly Diabetes, Research Support; Medtronic, Abbott, Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care, Inc. R.Wadwa: Consultant; Eli Lilly and Company, Other Relationship; Dexcom, Inc., Eli Lilly and Company, Research Support; Dexcom, Inc., Eli Lilly and Company, Beta Bionics, Inc., Tandem Diabetes Care, Inc. E.C.Cobry: None. Funding National Institutes of Health (5T32DK063687)

119-LB: Preliminary Accuracy Analysis of a Noninvasive Optical Glucose Sensing Prototype

Background: As the use of CGM in type 1 Diabetes (T1D) becomes more prevalent, it is important to develop less invasive technologies to improve diabetes health outcomes and reduce burden. Optical glucose sensing (OGS) offers a non-invasive option to measure sub-dermal glucose levels by shining a laser beam of light onto the skin. The aim of this study was to gather preliminary data on the accuracy of OGS technology using a prototype device. Methods: Eighteen adults with T1D (32 ± 11yrs.; 83% white, 17% Latinx) using Dexcom G6 or Libre CGM used an OGS prototype device for 5 days, scanning the palm of their hand up to eight times per day to measure glucose levels. Paired CGM values obtained from Clarity or Libreview CSV files were used for comparison. Outliers due to user-error were removed by leave-one-out cross-validation (20% of values). Results: Paired CGM and OGS values (n=599) resulted in a MARD of 19% ±13% with 99% of values within the Clarke Error Grid A&B Zones (Figure 1). The % within 15/15, 20/20, or 30/30 were 44%, 59%, and 80%, respectively. Discussion: OGS may be a promising, non-invasive alternative to current CGM devices on the market. Further research and development of the sensing algorithm is needed to lower the MARD closer to current factory calibrated CGMs. In addition, increased recruitment of people with diabetes who have darker skin tones is needed to evaluate accuracy across all populations. Disclosure L. Geiser: None. H. Leung: None. C. Berget: Consultant; Insulet Corporation, Dexcom, Inc. Other Relationship; Tandem Diabetes Care, Inc. C. Gong: None. Q. Liu: None. X. Zhou: None. G. P. Forlenza: Advisory Panel; Medtronic. Research Support; Medtronic, Abbott. Consultant; Dexcom, Inc. Research Support; Dexcom, Inc. Consultant; Insulet Corporation. Research Support; Insulet Corporation. Consultant; Tandem Diabetes Care, Inc. Research Support; Tandem Diabetes Care, Inc. Consultant; Lilly Diabetes. Funding National Science Foundation (ECCS-2037267)

Spam Email Detection Using Machine Learning Techniques

This paper focuses on the security of electronic mail, using machine learning algorithms. Spam email is unwanted messages, usually commercial, sent to a large number of recipients. In this work, an algorithm for the detection of spam messages with the aid of machine learning methods is proposed. The algorithm accepts as input text email messages grouped as benevolent (“ham”) and malevolent (spam) and produces a text file in csv format. This file then is used to train a bunch of ten Machine Learning techniques to classify incoming emails into ham or spam. The following Machine Learning techniques have been tested: Support Vector Machines, k-Nearest Neighbour, Naïve Bayes, Neural Networks, Recurrent Neural Networks, Ada Boost, Random Forest, Gradient Boosting, Logistic Regression and Decision Trees. Testing was performed using two popular datasets, as well as a publicly available csv file. Our algorithm is written in Python and produces satisfactory results in terms of accuracy, compared to state-of-the-art implementations. In addition, the proposed system generates three output files: a csv file with the spam email IP addresses (of originating email servers), a map with their geolocation, as well as a csv file with statistics about the countries of origin. These files can be used to update existing organisational filters and blacklists used in other spam filters.

#4310 DEVELOPMENT OF A HOME DIALYSIS PATIENT SUPPORT SYSTEM WITH EXERCISE FUNCTION PART I - SYSTEM DEVELOPMENT AND EVALUATION

Abstract Background and Aims Although many people use communications infrastructure, direct connection of electronic medical records to the Internet is restricted at medical institutions in Japan. Therefore, patient information outside the medical institution cannot be viewed on systems inside the medical institution. We are developing a safe and secure dialysis support system that connects dialysis patients and medical institutions, which is separate from the system within medical institutions. We have developed a dialysis support system that can share vital data, dialysis records, meal records, etc., between patients and medical institutions. In this study, to support dialysis patients’ exercise and its continuation, we created an ergo-storage device combining an ergometer and storage device for this research and added functions to the existing dialysis support system. This system with additional exercise function could encourage patients to exercise, store the amount of exercise as the amount of electricity used, this electricity represented a reward to the patient for exercising. These additional features allow the patient to exercise at home on both dialysis and non-dialysis days. Method An ergometer was used as exercise equipment for dialysis patients with a hub dynamo to convert the rotations of the ergometer through a belt into electricity. This allowed the dynamo to take over the rotation of the ergometer. In addition, we developed a new circuit that converts the power generated by the dynamo to USB voltage. A simple USB voltage and current checker was added to allow the integrated power to be displayed making it possible to visualize momentum as power generation. In addition, the generated power could be stored in the USB charger. To record the amount of exercise of the patient, the information from the ergometer was output to a tablet in CSV format. This CSV file was sent to the developed system and linked with the patient information in the existing system. This system made it possible to visualize and display the exercise information along with the patient's daily dialysis information and meal information on the developed system. Results Momentum could be converted to electrical energy using the ergo-storage device. The patient could now see and store the amount of exercise they have done, enabling the patient to exercise on both dialysis and non-dialysis days. As the amount of exercise could be visualized at a glance, the patient was encouraged and prompted to exercise daily. In addition, as the amount of exercise could be stored as electricity, this electricity represented a reward to the patient for exercising. The patients reported that it was good to be able to enjoy exercising instead of having a quota, and that they were able to continue exercising. Patients began exercising voluntarily instead of being forced to exercise by health care workers. In addition, it was possible to link the system with the patient's dialysis records and share the amount of exercise, making it easier for medical professionals to understand the patient's physical condition. Conclusion A function that allows dialysis patients to exercise was added to the developed system. Using this system, patients and medical institutions could share dialysis records, such as the patient's amount of exercise. The patients could exercise on both dialysis and non-dialysis days, and the amount of exercise and dialysis records could be shared with medical institutions. In addition, the amount of exercise was stored as electricity, which increased the patient's motivation to exercise. The patients began to exercise voluntarily. This research was supported in part by Gakushin Kaken (JP20H03982).

Digitizing Ottoman daily weather observations of Halkali Agricultural School in Istanbul, Turkiye (1896–1917)

AbstractDaily weather observations measured by students and staff at Halkali Agricultural School (a school opened in 1892 on agriculture and animal husbandry during the Ottoman period) from 1896 to 1917 in Istanbul, Turkiye have been transcribed from the original publications into digital form and translated from Ottoman Turkish (the Perso‐Arabic script) to English (Latin alphabet). Over 55,000 observations of daily maximum, minimum and average temperature; rainfall, soil and under soil (0.25 m) temperature; humidity, pressure and wind speed were recovered. In addition, weather observations taken in Kandilli Observatory and Earthquake Research Institute from 1911 to 1936 and taken in Florya Meteorological Station from 1937 to 2022 could inform long‐term temperature changes in Istanbul. The publication of a new historical data set that includes, for the first time, digitized and quality‐controlled daily meteorological observations in Istanbul will enhance the understanding of weather changes in Turkiye back to the late 19th century. These observations will be used to fill gaps in existing temperature and pressure records and to the improvement of the accuracy of reanalysis products prior to the 1950s. It will be the first data set publication of other parameters such as soil temperature, wind speed and humidity for that period in that region. Data are available on the CEDA Archive in csv file format.

Bibliometric Analysis: Six Decades of Scientific Production from a Nationwide Institution: Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado (ISSSTE) from Mexico.

This study employed bibliometric analysis to ascertain the research focus areas among a group of Mexican physicians affiliated with the Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado (ISSSTE). ISSSTE, a healthcare institution catering to a diverse range of diseases, offers a distinctive perspective on the investigated specialties within the realm of health. The primary objective was to identify knowledge gaps in medical care disciplines through a comprehensive examination of scholarly publications. We retrieved Scopus papers affiliated with "ISSSTE" and saved them as .CSV files. Subsequently, we employed VOSviewer, biblioshiny, and bibliometrix for bibliometric analysis. This enabled us to identify prominent institutions, prolific authors, highly cited researchers, and their respective affiliations. Our analysis identified 2063 publications; the specialty internal medicine accounted for the greatest proportion with 831 publications. Original papers accounted for 82% of the total, with 52% of them being written in Spanish. The majority of scientific output, 92%, originated from Mexico City. The annual production has steadily increased since 2010, peaking in 2021 with over 200 publications. However, papers on prevalent conditions, such as metabolic syndrome, received limited citations, and the L0 index (percentage of uncited items) for all papers is close to 60%. Scopus mislabeled one affiliation, and some cases show a low paper-to-author ratio of 0.5 Discussion: Additional concerns, such as honorary authorship due to excessive authors per paper, and the underlying causes of low citation rates in Mexican publications, warrant further examination. Moreover, our research emphasizes the urgency of bolstering research and development funding, which was consistently below 0.5% of GDP for the past four decades, falling short of legal mandates and international benchmarks. We endorse the establishment of robust research collectives in Latin America to address these challenges, foster regional scientific output, and transition from knowledge consumers to knowledge producers, thereby reducing dependence on foreign technology.

New Loan Payment Calculator Tool Launched

The Center for Ag Profitability has released its Loan Payment Calculator, a web-based tool that makes it easy to calculate annual loan payments. Users must enter the total purchase amount, down payment amount, annual interest rate (APR), duration in years, and the starting year of the loan. The calculator tool allows users to download the payment schedule, also called amortization, as a PDF or CSV file.

Capturing the Aftermath of the Dobbs v. Jackson Women’s Health Organization Decision in Google Search Results across the U.S.

How do Google Search results change following an impactful real-world event, such as the U.S. Supreme Court decision on June 24, 2022 to overturn Roe v. Wade? And what do they tell us about the nature of event-driven content, generated by various participants in the online information environment? In this paper, we present a dataset of more than 1.74 million Google Search results pages collected between June 24 and July 17, 2022, intended to capture what Google Search surfaced in response to queries about this event of national importance. These search pages were collected for 65 locations in 13 U.S. states, a mix of red, blue, and purple states, with respect to their voting patterns. We describe the process of building a set of circa 1,700 phrases used for searching Google, how we gathered the search results for each location, and how these results were parsed to extract information about the most frequently encountered web domains. We believe that this dataset, which comprises raw data (search results as HTML files) and processed data (extracted links organized as CSV files) can be used to answer research questions that are of interest to computational social scientists as well as communication and media studies scholars.

Bibliometric analysis of veterinary medicine on embryo of animals in textbook in conceptualizing disease and health

In veterinary medicine, the concept of disease is critical because it related to the survivability rate of the veterinary, especially livestock. Chicken was the most popular livestock that was observed in veterinary medicine. However, veterinary books were less popular compared to article and conference paper in global academic. This study's goal was to look how was the depiction of topic disease was used in veterinary textbooks that were related to the embryo of chicken as well as the trend of that topic. This study gathered 90 books meta-data donwloaded from Scopus website in form of CSV file. The data were analyzed using Vosviewer and biblioshiny of R Studio software to see the topic trend, citation, and number of book pages. Literature review also used to see the depiction of disease inside samples. Result showed that authors' keywords, heart and disease were closely related with a keyword chicken embryo. Moreover, each book get at least 10–11 citations globally. Moreover, repetitive keywords used in abstract of samples of this study were cells/cell, gene, and human. Those repetitive words were closely related to a word disease. It could be means that cell of the embryo of chicken also played the important role in determining its resistance against disease.