dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and methods Guanosine Triphosphatase (GT) activity encoded in NS4B protein's nucleotide binding motif (NBM) serves as single nucleotide variable in the evolution process of HCV and associated viral life cycle. The transforming activity of NS4B from HCV GT4 was analyzed using the mutants. Phylogenetic data on clustering of subtypes for E1 & NS5B genes was performed to identify the variation in viral genomes.
Materials and methods Caco2 cells were treated with 20 μM chelidonine for 48 h. RNA isolation and cDNA synthesis, hybridization, data quality assessment, filtering, normalization, and subsequent analysis were performed by procedure that meet or exceed the MIAME-criteria of microarray analysis  [1]. The numbers of significantly up and down regulated genes in treated Caco-2 cell were subjected to correspondence cluster analysis  [2]. Among the differentially expressed genes we selected highly P-value genes for Ingenuity Pathway Analysis Knowledge database (version 6. 5) (www. ingenuity. com) to identify the biological responses of treated colon adenocarcinoma.
Acknowledgements We would like to acknowledge the financial support provided by the  Sheikh Salem Bin Mahfouz Scientific Chair for Treatment of Osteoarthritis by Stem Cells. The  Center of Innovation in Personalized Medicine (CIPM) ,  Stem Cell Lab at Center of Excellence for Genomic Medicine Research and the Orthopedics Stem Cell Research Lab (CEGMR) at King Abdulaziz University Hospital  for supporting this study. Acknowledgements Authors would like to acknowledge the  Deanship of Scientific Research, King Abdulaziz University, Jeddah ,  Saudi Arabia  for funding the research (HiCi- 1434-117-2 ). Acknowledgements We acknowledge the fund provided by  KACST ( AT-35-438 ) and " Sheikh Salem Bin Mahfouz Scientific Chair for Treatment of Osteoarthritis by Stem Cells ",  King Abdulaziz University  and the  Orthopaedics department at KAUH  for providing the clinical material.
Acknowledgements  Dr. Muhammad Abu-Elmagd  project is funded by the  National Plan for Science, Technology and Innovation (MAARIFAH) -King Abdulaziz City for Science and Technology (KACST)  -the  Kingdom of Saudi Arabiaaward number  ( 13-CIPM-01 ). Acknowledgements This project was funded by the  National Plan for Science, Technology and Innovation (MAARIFAH) -King Abdulaziz City for Science and Technology, Saudi Arabiaaward number  ( 08-MED120-03 ). Acknowledgements This project was funded by the  National Plan for Science, Technology and Innovation (MAARIFAH) -King Abdulaziz City for Science and Technology, Saudi Arabiaaward number  ( 10-BIO1258-03 ,  10-BIO1073-03  and  08-MED120-03 ). Acknowledgements Authors would like to acknowledge  Deanship of Scientific Research, Jeddah ,  Saudi Arabia  ( HiCi-1434-117-2 ) for funding the research.
Acknowledgements The financial support provided by  King Abdulaziz City for Science and Technology (KACST)  ( AT-35-437 ), the " Sheikh Salem Bin Mahfouz Scientific Chair for Treatment of Osteoarthritis by Stem Cells " ( 11-557 ,  DSR ,  KAU ) as well as the logistics provided by the  Stem Cells Lab at CEGMR ; the  Obstetrics & Gynaecology Department of the King Abdulaziz university Hospital  and technical support by the flow cytometry unit are greatly acknowledged. Acknowledgements The financial support provided by  KACST  ( AT-35-436 ), the  "Sheikh Salem Bin Mahfouz Scientific Chair for Treatment of Osteoarthritis by Stem Cells  ( 11-557 ,  DSR, KAU )", the training and support by the  Stem Cells Unit  and the flow cytometry unit at CEGMR and the clinical material provided by the  Department of Orthopaedics, King Abdul Aziz University Hospital  are greatly acknowledged.
Prognostic significance of CD44 expression profile in colorectal carcinoma Maryam Al-Zahrani 1  ,  Sahira Lary 1 , Sahar Hakamy 2 , Ashraf Dallol 2 , Mahmoud Al-Ahwal 3 , Jaudah Al-Maghrabi 4 , Emmanuel Dermitzakis 5 , Adel Abuzenadah 2 , Abdelbaset Buhmeida 2 , Mohammed Al-Qahtani  2 Acknowledgements This project was supported by the  NSTIP strategic technologies program in the Kingdom of Saudi Arabia-Project No. ( 12-BIO2725-03 ). The authors also acknowledge with thanks  Science and Technology Unit, King Abdulaziz University  for technical support. Acknowledgements This financial support by  King Abdulaziz City for Science and Technology (KACST)  through  postgraduate grant funding  ( AT-34-237 ) is greatly acknowledged. Acknowledgements The  Department of Orthopaedics at the King Abdulaziz University Hospital, King Abdulaziz University  and the " Sheikh Salem Bin Mahfouz Scientific Chair for Treatment of Osteoarthritis by Stem Cells " which provided the clinical material are greatly acknowledged.
Acknowledgements This work was funded by the  National Plan for Science, Technology and Innovation (MAARIFAH) -King Abdulaziz City for Science and Technology -the Kingdom of Saudi Arabiaaward numbers  ( 12-BIO2719-03 ) and ( 12-BIO2267-03 ). The authors also, acknowledge with thanks  Science and Technology Unit, King Abdulaziz University  for their excellent technical support.
Acknowledgements The financial support provided by  King Abdulaziz University (KAU) ,  Jeddah , under grant number  4/00/00/252  and the " Sheikh Salem Bin Mahfouz Scientific Chair for Treatment of Osteoarthritis by Stem Cells " which provided the clinical material are greatly acknowledged. 1  King Fahd Medical Research Centre (KFMRC) ,  King Abdulaziz University, Jeddah ,  Saudi Arabia; 2 Centre of Excellence in Genomic Medicine Research (CEGMR) ,  King Abdulaziz University, Jeddah ,  Saudi Arabia ; 3  Aziziah Maternity and Children Hospital, Jeddah ,  Saudi Arabia ; 4  Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia; 5 Center of Innovation in Personalized Medicine, King Abdulaziz University, Saudi Arabia; 6 Sheikh Salem Bin Mahfouz Scientific Chair for Treatment of Osteoarthritis by Stem Cells, King Abdulaziz University, Jeddah, Saudi Arabia; 7 Department of Orthopaedic Surgery, Faculty of Medicine, King Abdulaziz University Hospital, Jeddah ,  Saudi Arabia ; 8 The  D-BOARD European Consortium for Biomarker Discovery , The  APPROACH Innovative Medicines Initiative (IMI) Consortium, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey ,  GU2 7XH , UK; 9  Arthritis Research UK Centre for Sport, Exercise and Osteoarthritis, Arthritis Research UK Pain Centre, Medical Research Council and Arthritis Research UK Centre for Musculoskeletal Ageing Research, University of Nottingham, Queen's Medical Centre, Nottingham ,  NG7 2UH , UK Correspondence:  Gauthaman Kalamegam  (kgauthaman@kau.
Acknowledgements This work was supported by the  Deanship of Scientific Research (DSR ;  D1435-573-662 ),  King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia. Authors gratefully acknowledge technical and financial support of  DSR. Acknowledgements This work was supported by generous funds from the  National Plan for Science, Technology and Innovation (MAARIFAH) ,  King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabiaaward number  ( 09-BIO-693-03 ).
Acknowledgements We express our gratitue and appreciation for the financial support provided by  King Abdulaziz City for Science and Technology (KACST)  postgraduate student grant [AA-]. 1  Center of Excellence in Genomic Medicine Research (CEGMR) ,  King Abdulaziz University, Jeddah ,  Saudi Arabia ; 2  Center of Innovation in Personalized Medicine, King Abdulaziz University, Jeddah, Saudi Arabia; 3 Gynecological Oncology Unit, Department of Obstetrics and Gynaecology, Faculty of Medicine, King Abdulaziz University Hospital, Jeddah ,  Saudi Arabia Correspondence :  Mourad Assidi  (mourad. assidi@gmail. com)- Center of Innovation in Personalized Medicine, King Abdulaziz University, Jeddah ,  Saudi Arabia BMC Genomics 2016 ,  17(Suppl 6 ):P112 Acknowledgements We sincerely thank the Chair " Abdullah Basalamh of the women's Tumors ",  King Abdulaziz University  for supporting this study.
Acknowledgements This work was funded by the  National Plan for Science, Technology and Innovation (MAARIFAH) -King Abdulaziz City for Science and Technologythe Kingdom of Saudi Arabiaaward number  ( 12-BIO2267-03 ). The authors also, acknowledge with thanks  Science and Technology Unit, King Abdulaziz University  for their excellent technical support. Acknowledgements This work was supported by generous funds from the  National Plan for Science, Technology and Innovation (MAARIFAH) ,  King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabiaaward number  ( 09-BIO-693-03 ). Acknowledgements We would like to acknowledge the  Deanship of Scientific Research, King Abdulaziz University, Jeddah ,  Saudi Arabia  for funding the research (HiCi- 1434-117-2 ). Acknowledgements This study was supported by  King Abdulaziz City for Science and Technology (KACST)  grant  AT-32-98.
Acknowledgements This study was supported by  King Abdulaziz City for Science and Technology (KACST)  grant  AT-32-98  Acknowledgements This study was performed in support of the  King Abdulaziz City for Science and Technology (KACST)  grant  09-BIO2289-03. Acknowledgements This study was performed in support of the  King Abdulaziz City for Science and Technology (KACST)  grant  09-BIO2289-03. Acknowledgements This project was funded by the  National Plan for Science, Technology and Innovation (MAARIFAH) -King Abdulaziz City for Science and Technologythe Kingdom of Saudi Arabiaaward number  ( 12-BIO3082-03 ). The authors also acknowledge with thanks  Science and Technology Unit, King Abdulaziz University  for technical support. P129 Integrated analysis reveals association of ATP8B1 gene with colorectal cancer  Mohammad A Aziz 1 , Rizwan Ali 1 , Mahmood Rasool 2 , Mohammad S Jamal 3 , Nusaibah samman 1 , Ghufrana Abdussami 4 , Sathish Periyasamy 1 , Mohiuddin K Warsi 5 , Mohammed Aldress 1 , Majed Al Otaibi 1 , Zeyad Al Yousef 1 , Mohamed Boudjelal 1 , Abdelbasit Buhmeida 2 , Mohammed H Al-Qahtani 2 , Ibrahim AlAbdulkarim 1 Acknowledgements  This project was funded by the  National Plan for Science, Technology and Innovation (MAARIFAH) -King Abdulaziz City for Science and Technologythe Kingdom of Saudi Arabiaaward number  ( 12-BIO3082-03 ).
University, Rabigh, Kingdom of Saudi Arabia; 4 Department of Obstetrics and Congenital heart diseases (CHDs) represent a major category of Gynecology, Faculty of Medicine, King Abdulaziz University, Jeddah, Kingdom world-wide birth defects. We had searched MEDLINE databases, of Saudi Arabia; 5 KACST Technology Innovation Center in Personalized clinical-science journals and reports from the earlier reviews. We used Medicine, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia the search term "Congenital heart diseases", "Saudi Arabia", "Down Correspondence: Mohd A Beg (mabeg51@gmail. com) -King Fahd syndrome", "Molecular genetics". Medical Research Center, King Abdulaziz University, Jeddah, Kingdom of Results Saudi Arabia CHDs were reported in about 6-13 per 1000 live births and implicated BMC Genomics 2016, 17(Suppl 6):P100 in increased incidence of early childhood mortality.
We describe how to estimate progression-free survival while dealing with interval-censored data in the setting of clinical trials in oncology. Three procedures with SAS and R statistical software are described: one allowing for a nonparametric maximum likelihood estimation of the survival curve using the EM-ICM (Expectation and Maximization-Iterative Convex Minorant) algorithm as described by Wellner and Zhan in 1997; a sensitivity analysis procedure in which the progression time is assigned (i) at the midpoint, (ii) at the upper limit (reflecting the standard analysis when the progression time is assigned at the first radiologic exam showing progressive disease), or (iii) at the lower limit of the censoring interval; and finally, two multiple imputations are described considering a uniform or the nonparametric maximum likelihood estimation (NPMLE) distribution.
Some of these methods are implemented in statistical software (6): the Expectation-Maximization (EM) algorithm  (7) , the Iterative Convex Minorant (ICM) algorithm  (8) , and the EM-ICM algorithm  (9). Although tutorials for them exist, some may be obsolete or not only for medical research purposes  (10, 11). Although these methods are now widely implemented and recommended in oncology by the European Medicines Agency and the U. S. Food and Drug Administration as part of a sensitivity analysis  (12) (13) (14) , only a few clinical trials have assessed PFS with methods that take interval-censored data into account  (15) (16) (17) (18) (19). To make these methods as accessible as possible, we provide a step-by-step guide from data formatting to output interpretation of interval-censoring methods using SAS and R software.
To do so, each step was conducted in the database of a real phase II clinical trial assessing the efficacy of a first-line therapeutic regimen for patients with metastatic colorectal cancer. nonrandomized phase II trial were reanalyzed. They received biweekly a 90-minute intravenous infusion of bevacizumab (5 mg/kg), followed by a 90-minute intravenous infusion of irinotecan (180 mg/m 2 ), followed by a simplified LV5FU2 regimen [leucovorin (400 mg/m 2 ) and bolus fluorouracil (400 mg/m 2 ) on day 1 and a 46-hour infusion of fluorouracil (2,400 mg/m 2 )] (20).
Data formatting PFS was defined as the time from study entry to the occurrence of the "event," namely progression or death. To compute any type of survival analyses, data have to be properly formatted with regard to the following: -Complete observations dataset: For PFS, this means that all patients have progressed or died, and the only variable needed is the time elapsed until the occurrence of the event. This is really rare in clinical studies. -Right-censored dataset: This is when the follow-up was not long enough to allow all events to occur during the study. In such cases, two variables are needed in the dataset: a status variable to know whether the event occurred (status ¼ 1) or not (status ¼ 0) and a time variable describing the time elapsed until the last news (if status ¼ 0) or until the occurrence of the event (if status ¼ 1).
-Interval-censored dataset: This means that the exact moment at which the event occurred is unknown, although there has been a time interval during which the event occurred. This is the focus of this article. In such cases, two variables are needed in the dataset: T1 and T2. The time period between T1 and T2 is the interval during which the event occurred. Data have to be formatted as follows: & For patients who died without progression (uncensored data): * T1 ¼ T2 ¼ survival.
-"Multiple imputations": In parallel, we decided to present two multiple imputation methods to estimate PFS and confidence interval (CI), as the issue of interval censoring may be regarded as a missing data problem. The first one consisted of imputed data according to a uniform distribution in the censoring interval, and the second one consisted of imputed data according to the NPMLE distribution. Those methods might be less time-consuming than the interval-censored procedure presented above, but they are not straightforward when implemented in SAS and R. Thus, we have decided not to present them in details, but to provide only the results to compare them with the two other methods we proposed. For the sensitivity procedure, CIs of PFS estimation were computed on the basis of the log hazard, namely log(Àlog(PFS)).
Statistical analyses were done using the SAS software 9. 3 (Copyright 2002-2010 SAS Institute Inc. ) and the R-3. 0. 1 software (Copyright 2014 The R Foundation for Statistical Computing) with interval, survival, splines, perm, Icens, and MLEcens packages  (22) (23) (24) (25) (26) (27) (28). The dataset used for this article is available in Supplementary Material S1. SAS and R input and output are presented in Supplementary Materials S2 and S3. The source code of multiple imputations methods is available in Supplementary Material S4.
Interval-Censored Procedure In SAS PFS estimation. If version 13. 1 of SAS/STAT in SAS 9. 4 TS Level 1M1 is installed, the ICLIFETEST procedure may be used with the help of the well-described SAS support  (29). Otherwise, you may use the EMICM macro, which may be downloaded from its help page (http://support. sas. com/kb/24/980. html). The macro has to be first compiled in SAS before running it with the data imported: %EMICM(data¼WORK. MyData, left¼T1, right¼T2, method¼EMICM, out¼intervalPFS, options¼plot, mRS¼100); Outputs should appear in the Results Viewer tab, and a new table "Intervalpfs" should be created in the work library, containing survival probabilities and SEs for each time interval defined with "Lower" and "Upper" columns.
Midpoint of the interval In R. MyData <within(MyData, status <ifelse(is. na(T2), 0, 1)) MyData <within(MyData, midpoint <ifelse(is. na(T2), T1, (T2þT1)/2)) Now the "survfit" function of the "survival" package may be used to estimate the "midpoint" PFS over time: midpointPFS <-survfit(Surv(midpoint, status)$1, data ¼ MyData, conf. type¼ "log-log") Outputs of this "midpoint" PFS are now saved in the "mid-pointPFS" object. The estimation is summarized with: midpointPFS Then the PFS estimation may be plotted over time by calling: summary(midpointPFS) plot(midpointPFS).
DATA WORK. MyData; set WORK. MyData; upper¼midpoint; if T2 NE. AND T1 NE. then upper¼T2; run; The LIFETEST procedure may then be used to estimate the upper PFS over time, as described in the midpoint procedure section, while replacing the "midpoint" variable by the "upper" variable. In R. MyData <within(MyData, upper <ifelse(!is. na(T2) & !is. na(T1), T2, midpoint)) The "survfit" function of the "survival" package may then be used to estimate the upper PFS over time, as described in the midpoint procedure section, while replacing the "midpoint" variable by the "upper" variable.
Interval-censored procedure The shape of PFS assessed by an, NPMLE taking into account interval-censored data is similar to a Kaplan-Meier curve except for gray rectangles. In fact, as the exact PFS is unknown (no unique solution) in these periods of time, the rectangles display all the possibilities that PFS may be. For each time interval, output tables returned by SAS and R display PFS probability. The main difference between them only relies on the display: In R output, there are less time intervals than in SAS output because consecutive time intervals with the same PFS probabilities are pooled together. To estimate median PFS, the point where the survival probability reaches 0. 5 must be established, namely during the time interval of 9. 66 to 9. 79 months. As the PFS is between 0. 5329 and 0.
95% CI of median PFS. In SAS. It is not easy to estimate a CI of the median PFS with the EMICM macro, but SEs of survival estimations are available for each time interval. In R. To find the 95% CI of this median, the same must be done in both columns of the 95% CI of survival over time (lower and upper bound). -For the lower bound, one may call: lower<-with(intervalPFS$CI,{i<-which( lower¼¼min(lower[lower>¼0. 5])); i<-c(i,tail(i,1)þ1); data. frame The probability at 5. 65 months falls from 0. 53 to 0. 48. So, in Fig. 1 , the horizontal line representing a PFS of 0. 5 (definition of median survival) cuts the lower line of the CI at this time. (time¼round(time[i],2), lower¼ lower[i])}) -For the upper bound, the same code may be called while replacing lower by upper: The probability at 10. 18 months is 0.
55, and at 13. 54 months it is 0. 37. So, the probability 0. 5 is reached within this time interval, at 11. 07 months. PFS estimation at a precise time. One may also need to estimate PFS at a precise time. To do so, the interval to which this time belongs must be found. For example, at 12 months, which belongs to the time interval of 10. 18 to 13. 54 months, as seen in the R output, or to 11. 79 to 12. 65 months, as seen in the SAS output, the associated survival probability is 0. 248. At 10 months, which is not within any interval, the associated survival probability is between 0. 338 and 0. 248.
Sensitivity analysis procedure In this procedure, PFS is assessed using the standard Kaplan-Meier estimator. The median PFS is thus directly estimated by SAS and R (Table  2 ). To estimate PFS at a precise time, the nearest time that is lower than or equal to the target time must be found in the output table. For example, at 12 months, midpoint PFS is the same as at 11. 385 months, namely 0. 259 [95% CI (0. 155-0. 375)]. As described above for the midpoint PFS procedure, the estimation of the median PFS while considering the upper limit of the censoring interval is 10. 215 months (95% CI, 8. 310-11. 530; Table  2 ). At 12 months, PFS is 0. 310 (95% CI, 0. 197-0. 430). The estimation of the median PFS while considering the lower limit of the censoring interval is 7. 62 months, (95% CI, 5. 75-9.
Discussion In this article, we explain how to estimate PFS by using three different methods with interval-censored data using SAS and R statistical software and applied to a real dataset of patients with metastatic colorectal cancer enrolled in a phase II clinical trial. As expected, the standard procedure (which is the procedure considering progression at the lower limit of the censoring interval) overestimates PFS in this example. In clinical trials with short time intervals between visits, this error may be negligible. The "midpoint procedure" is an easy-to-use method, but its scope of use is limited when used alone  (36). We have, therefore, proposed a sensitivity analysis with the three systematic imputations: midpoint, upper limit, and lower limit of the interval, as recommended by Panageas and colleagues  (21).
The results obtained with the sensitivity analysis are similar to the results obtained with the other two procedures, however more complex. The three procedures proposed here (the sensitivity analysis procedure, the interval-censored procedures, and the multiple imputation methods) require that the time of last visit before progression of each patient has to be recorded in the dataset, which is not always the case in the case report form. Other methods, such as the maximum bias, exist to analyze the worst-case scenario. Beyond the wish to estimate PFS without bias, all the uncertainty in its variance estimate has to be taken into account. One solution is to add a bootstrap resampling stage, but it makes the procedures more time-consuming. Semiparametric maximum likelihood estimators or methods for grouped data (where all subjects follow the same assessment schedule) may be preferred in the case of large dataset  (4, 37, 38).
Finally, caution is required when dealing with cancer types where progression is not only assessed by radiologic techniques. In settings such as gastrointestinal stromal tumor, progression may be established during a clinical examination, so the interval to which the exact time of progression belongs may be unknown. However, it would certainly be shorter than with radiologic exams. Thus, we do not recommend the use of methods dealing with interval-censored data when these intervals are unknown or when the censoring mechanism is not independent of the event process. As the dataset used here is available from the Supplementary Material, readers may practice before using such methods on their own data.
time & For patients who have not progressed or died during the study (right-censored data): T2 ¼ infinite time (missing data for SAS or R) & For patients who have progressed (interval-censored data): * T1 ¼ time elapsed to last visit showing no progression * T2 ¼ time elapsed to first visit showing disease progression If only a few or no patients are interval censored in the dataset, it is pointless to use methods that deal with interval-censored data. * T1 ¼ time elapsed to last visit *.