dataesr/scientific-paragraphs-categorization huggingface dataset:
Such a stabilization effect was explained by the existence of some specific interactions between both drugs. Accordingly, one can expect that, in the EZB/SVT system, the source of the observed stabilization is analogous to the aforementioned cases. The amorphous form of EZB/SVT composition stored at standard storage conditions was measured by means of XRD at the beginning once daily for a week, later once a week for a month, and then once a month until 1 year. As can be clearly seen even after 1 year, the examined EZB/SVT system did not reveal any sign of re-crystallization. Comparing this result with the previously published by Knapik et al. , data for pure EZB, one can conclude that SVT impressively stabilizes EZB. It should be emphasized that herein the improvement of physical stability was achieved despite acceleration the EZB's molecular mobility.
During these studies, the mixture's XRD patterns were collected every 30 min for 60 h. The obtained results are presented in Figure  6. The XRD patterns of both freshly vitrified as well as stored at elevated temperature conditions for 60 h EZB/SVT + 5wt. % KVA mixture reveal a lack of Bragg's peaks. This indicates that the sample remained amorphous throughout the duration of the experiment. Comparing this result with the data presented in Figure  3c , one can conclude that KVA significantly improves the physical stability of the amorphous EZB/SVT system at elevated temperature conditions (T = 373 K).
As data presented in Figure  9  proves, the investigated EZB/SVT/KVA system was for all Pharmaceuticals 2019, 12, 40 10 of 15 that time fully amorphous. The last stage of the XRD measurement consisted in keeping the cooled down to T = 298 K ternary composition for a period of 3 months. The sample's diffraction patterns were collected one a day for a week, next once a week for a month, and finally once a month for 3 months. Data presented in Figure  9  indicates that the examined ternary system is physically stable for at least 3 months after amorphization and annealing at T = 373 K for 30 h. range, which are suitable to produce physically-stable amorphous EZB/SVT system by means of the melting methods. For example, in the case of small-scale extrusion, where the recommended material's viscosity range between 800-10,000 Pa•s, the lowest available concentration is equal to 40wt.
% KVA sample by means of XRD. The representative XRD patterns obtained during this experiment are presented in Figure  9. As can be seen, the diffraction pattern of the freshly prepared sample (quenched from T = 398 K) do not reveal sharp Bragg's peaks, indicating that T = 389 K is high enough to fully melt the sample. In the next step of the XRD experiment, the vitrified sample has been heated up to 373 K and stored for 30 h. After each 30 min, the XRD pattern has been registered. As data presented in Figure  9  proves, the investigated EZB/SVT/KVA system was for all that time fully amorphous. The last stage of the XRD measurement consisted in keeping the cooled down to T = 298 K ternary composition for a period of 3 months. The sample's diffraction patterns were collected one a day for a week, next once a week for a month, and finally once a month for 3 months.
X-ray Diffraction (XRD) X-ray diffraction experiments were performed for samples stored at both ambient and elevated temperature conditions on a Rigaku-Denki D/MAX RAPID II-R diffractometer (Rigaku Corporation, Tokyo, Japan) with a rotating anode Ag KR tube (λ = 0. 5608 Å), an incident beam (002) graphite monochromator, and an image plate in the Debye-Scherrer geometry. The pixel size was 100 µm × 100 µm. Measurements were performed on sample-filled and empty capillaries, to allow subtraction of the background intensity. The beam width at the sample was 0. 1 mm. The two-dimensional diffraction patterns were converted into one-dimensional intensity data versus the scattering angle.
Background: Therapeutic efficacy studies in uncomplicated Plasmodium falciparum malaria are confounded by new infections, which constitute competing risk events since they can potentially preclude/pre-empt the detection of subsequent recrudescence of persistent, sub-microscopic primary infections. Methods: Antimalarial studies typically report the risk of recrudescence derived using the Kaplan-Meier (K-M) method, which considers new infections acquired during the follow-up period as censored. Cumulative Incidence Function (CIF) provides an alternative approach for handling new infections, which accounts for them as a competing risk event. The complement of the estimate derived using the K-M method (1 minus K-M), and the CIF were used to derive the risk of recrudescence at the end of the follow-up period using data from studies collated in the WorldWide Antimalarial Resistance Network data repository.
Identification of studies for potential inclusion The WorldWide Antimalarial Resistance Network (WWARN) repository contains a large collection of standardized data on antimalarial drugs  [13]. Studies in the WWARN data repository were eligible for inclusion in the current analysis, if the data were from prospective clinical efficacy studies of uncomplicated P. falciparum (alone or mixed infections with Plasmodium vivax) in which patients were treated with one of the following fixed-dose regimens: artemether-lumefantrine (AL), dihydroartemisinin-piperaquine (DP), artesunate-amodiaquine (ASAQ), or artesunate-mefloquine (ASMQ). All studies also had to have applied molecular PCR genotyping to distinguish recrudescence from new infection. Studies on prophylactic use of antimalarials, severe malaria, pregnant women, patients with hyperparasitaemia, healthy volunteers, and travellers were excluded.
1a ). This assumption of identical parasite multiplication rates might be plausible for rapidly eliminated drugs. For slowly eliminated drugs, the parasite growth rate for recrudescence and a new infection are likely to be different. Similarly, a recrudescent infection where the parasite numbers never go below those encountered at the beginning of a new infection cannot be pre-empted and the occurrence of recrudescence will not be affected (Fig. 1b ). The only exception is if the new infection is more resistant than the primary infection. When the initial infection is completely eliminated after exposure to antimalarials, the host is no longer at a risk of subsequent recrudescence and competing risk situation does not exist (Fig. 1c ). In reality it is impossible to disentangle the underlying in vivo parasitological circumstances from this dataset.
Acknowledgements We would like to thank the patients and all the staff who participated in these clinical trials at all the sites. We would like to thank  Dr. Charlie Woodrow ,  Dr. Louis K Penali ,  Dr. Adoke Yeka ,  Dr. Issaka Sagara ,  Dr. Oumar Faye ,  Dr. Khadime Sylla ,  Dr. Roger Tine ,  Prof. Paul Newton , and  Dr. Nathalie Strub  for their kind.
Background: Artesunate (ART) is an artemisinin derivative used as monotherapy for the treatment of severe malaria and in combination with a partner drug for non-severe malaria. Resistance of malaria parasites to artemisinins have emerged in Southeast Asia. Adjustment of drug regimen may be an option to prevent therapeutic failures considering the relative favourable safety profile of ART high doses. Methods: For that purpose, a systematic review was done using PubMed, Scopus and Web of Science databases. All studies on ART and DHA pharmacokinetic post-administration of artesunate in human patients or volunteers were included. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist 2009 was used. Findings: Fifty studies exploring oral, intravenous, rectal, and intramuscular route (1470 persons, volunteers and patients) were included.
Bibliographic research An extensive search for studies published before January 2019 was performed using three databases: MEDLINE, Web of Science (WoS) and Scopus. Keys words of interest were "artesunate" and "pharmacokinetic". Search details are as follows: ("artesunate" No filter or date limitations were used. To increase the completeness of the search, a snowballing approach was used. The duplicates were removed. The remaining papers were then assessed for eligibility by one author (YIK), based on their abstract, title and key words. The full texts of the eligible records were assessed to determine those to be included. Inclusion and exclusion criterion were established a priori. All studies on ART and DHA pharmacokinetic postadministration of ART in human patients or volunteers were included.
Data management The PRISMA checklist 2009 was used as a methodological support of the systematic review. PK data were extracted from the included studies by one author (YIK) and an independent sampling of those data was done by another author (AL) to ensure their correctness. Information collected included first author, year of publication, type and geographical origins of subjects included, ART and DHA PK parameters, ART regimens and the sampling and analytical protocols. When different physiological statuses (adults, pregnant women, children) were mixed in the same cohort, the PK data of such cohort were not collected. PK parameters expressed as medians were not used in statistical analysis. When the dose expressed in mg/kg was not available but the mean weight of a population group was, it was calculated.
Recording, Reporting and Data Verification ICMVs record testing and treatment data in ICMV register which contains four copies. They keep one copy and send other copies to EHO head offices where data staff single-enter the data using ACCESS software. Then, EHO head offices send one copy to NMCP and one copy to CPI. Monitoring and evaluation (M&E) supervisors of EHOs check data completeness, accuracy, reliability and timeliness at the time of collecting malaria carbonless registers from ICMVs. They also perform random checking for data consistency between the electronic data set and malaria carbonless registers monthly. CPI M&E team performs routine data quality assessment at EHO offices and volunteer residence quarterly. CPI M&E team members perform data verification by recounting the reported numbers in the source documents and random checking of indicators across electronic database and source documents.
Data Variables and Source of Data Data in electronic format were extracted from the ACCESS database (see Annex S1). Data variables used in this study were EHO identifier, state/region, service delivery type (health posts, mobile team and village-based ICMV), age group, gender, presentation within 24 h of fever onset (yes/no), RDT result (Plasmodium falciparum-Pf, Plasmodium vivax-Pv, mixed), pregnancy (if female), severe malaria (yes/no), referred (yes/no), and number of artesunate combination tablets, chloroquine and primaquine tablets dispensed.
Data Analysis The electronic data set was imported into STATA (version 12. 1 STATA Corp. , College Station, TX, USA) for analysis. Frequencies and proportions (95% confidence interval (CI)) were used to assess the key outcomes. Association of baseline characteristics with non-receipt of correct treatment and non-receipt of 'correct and timely' treatment were assessed using unadjusted and adjusted prevalence ratios and 95% CIs. Modified Poisson regression with robust variance estimation was used for the adjusted analysis and all the available variables were included. Because of the large sample size, factors were assessed for significance by first looking at the programmatically significant association (adjusted prevalence ratio >1. 5 or <0. 7) and then looking at the statistical significance (p < 0.
Strengths and Limitations A study based on the nationwide malaria data of NMCP (2015) had assessed the correctness of malaria treatment. The number of tablets dispensed according to the type of malaria, age group and pregnancy status was used to assess the correctness of treatment  [15]. The definition used in our study was more rigorous and we also used 'eligibility for referral', 'referred or not' and timeliness (among those with correct treatment) to assess the quality of malaria. We were able to analyse the reasons for incorrect treatment as EHOs had good documentation practices. There were some limitations. The risk factors assessed were limited to the variables available in records and other socio-demographic, programmatic and health system level factors were not assessed. Second, the study assessed only the prescription of the complete course of drugs (number of each type of tablet) at the time of first dose prescription.
Acknowledgments: This research was conducted through the  Structured Operational Research and Training Initiative (SORT IT ), a global partnership led by the  Special Programme for Research and Training in Tropical Diseases at the World Health Organization (WHO/TDR ). The model is based on a course developed jointly by the  International Union against Tuberculosis and Lung Disease  ( The Union ) and Medecins Sans Frontieres (MSF/Doctors without Borders). The specific SORT IT programme which resulted in this publication was jointly organised and implemented by  The Centre for Operational Research, The Union, Paris, France ;  Department of Medical Research, Ministry of Health and Sports, Yangon; Department of Public Health, Ministry of Health and Sports, Nay Pyi Taw; The Union Country Office, Mandalay, Myanmar ;  The Union South-East Asia Office, New Delhi, India and London School of Hygiene and Tropical Medicine, London, UK.
e. , foam. However, due to the low viscosity of BSA solution (data not shown), the obtained foam collapsed rapidly. The addition of ALG to the solution enhanced the system viscosity, allowing the formation of foams with better stability. As the Maillard reaction was associated with viscosity improvement, a comparable enhancement in the foam stability was also recorded. When ALG solution is added dropwise to certain divalent cation solutions, such as calcium and zinc, the cations diffuse inside the ALG droplets and crosslink the ALG chains causing ALG gelation and the formation of beads  [13, 29]. Dispersing a drug in ALG solution would result in ALG beads loaded with drug. The high foamability and foam stability of BSA-ALG systems (physical mixture (0 h) and 24 h conjugates) suggested the possible creation of ALG beads using the foam as the starting system instead of a traditional solution.
Our patient had typical ELP with a compatible CT scan and BAL. Although it has never been described in the literature, the most probable culprit in this context was the 300 mg non-gastro-resistant capsules of ASU capsules  [15] [16] [17]. We consulted the international databases of "Expanscience Laboratoires", the manufacturer of the drug, looking for similar adverse effect in the drug safety databases, but we did not find any. Although the patient's cough may arguably have been due to the ELP itself, its nocturnal timing and the GERD-compatible 24-h pH-metry suggested symptomatic nocturnal GERD. ELP is often insidious, and in our patient, the initial lingular opacity could be an inaugural sign of the disease  [2] , even though no low-attenuation areas were seen within it. Both PET scans did not show any abnormal uptake.
Drug combinations are of great interest for cancer treatment. Unfortunately, the discovery of synergistic combinations by purely experimental means is only feasible on small sets of drugs. In silico modeling methods can substantially widen this search by providing tools able to predict which of all possible combinations in a large compound library are synergistic. Here we investigate to which extent drug combination synergy can be predicted by exploiting the largest available dataset to date (NCI-ALMANAC, with over 290,000 synergy determinations). Each cell line is modeled using primarily two machine learning techniques, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), on the datasets provided by NCI-ALMANAC. This large-scale predictive modeling study comprises more than 5,000 pair-wise drug combinations, 60 cell lines, 4 types of models, and 5 types of chemical features.
These are implemented in various commercial and publicly available software kits for the analysis of combination data, e. g. ,  Combenefit (Di Veroli et al. , 2016) , CompuSyn (http://www. combosyn. com) or CalcuSyn (http://www. biosoft. com/w/calcusyn. htm). One major roadblock in drug synergy modeling has been the lack of homogeneous data (i. e. , datasets generated with the same assay, experimental conditions and synergy quantification). This has been, however, alleviated by the recent availability of large datasets from High-Throughput Screening (HTS) of drug combinations on cancer cell lines. For instance, Merck has released an HTS synergy dataset  (O'Neil et al. , 2016) , covering combinations of 38 drugs and their activity against 39 cancer cell lines (more than 20,000 measured synergies).
This dataset has been used to build predictive regression and classification models using multiple machine learning methods  (Preuer et al. , 2018). AstraZeneca carried out a screening study, spanning 910 drug combinations over 85 cancer cell lines (over 11,000 measured synergy scores), which was subsequently used for a DREAM challenge  (Li et al. , 2018; Menden et al. , 2019). Very recently, the largest publicly available cancer drug combination dataset has been provided by the US National Cancer Institute (NCI). This NCI-ALMANAC  (Holbeck et al. , 2017)  tested over 5,000 combinations of 104 investigational and approved drugs, with synergies measured against 60 cancer cell lines, leading to more than 290,000 synergy scores (ComboScores). NCI-ALMANAC datasets have recently been modeled to predict the best growth inhibition of a given drug combinationcell line tuple  (Xia et al.
Data NCI-ALMANAC is the largest-to-date phenotypic drug combination HTS. It contains the synergy measurements of pairwise combinations of 104 FDA approved drugs on the 60 cancer cell lines forming the NCI-60 panel  (Shoemaker, 2006). The drugs include a wide array of small organic compound families, as well as several inorganic molecules (cisplatin and related platinum-organic compounds, arsenic trioxide). A similarity clustering dendrogram (Figure  1 ) shows the high diversity of the drugs in NCI-ALMANAC. Indeed, only 3 clusters comprising 8 drugs are formed with a Tanimoto score threshold of 0. 8 (Vinblastine with Vincristine, Sirolimus and Everolimus, and Daunorubicin-Doxorubicin-Idarubicin-Epirubicin clusters), while the remaining 96 drugs have smaller similarity among them. NCI-ALMANAC aggregates synergy data from three screening centers: NCI's Frederick National laboratory for Cancer Research (screening center code 1A, 11,259 synergy determinations), SRI International (FF, 146,147 determinations), FIGURE 1 | Sketch of the workflow for drug combination modeling.
Training data comes from NCI-ALMANAC, which comprises over 290,000 synergy measurements from pairs of 104 drugs tested on the 60 cell lines. Structural and physico-chemical features are calculated for each drug from its chemical structure. Similarity clustering diagram for 104 NCI-ALMANAC drugs is on the left. Each drug is characterized by MFPC features complemented with physico-chemical features, using the Tanimoto score on these features as the similarity metric. Hierarchical agglomerative clustering was carried out (ward. D2 algorithm in the R hclust function). Closely related compounds form tight clusters (e. g. , doxorubicin and its analogs, analogs of paclitaxel, etc). By contrast, naturally inorganic compounds such as cisplatin and arsenic trioxide appear as outliers (the highest similarity coefficient to other drugs being 0.
Since there is no overlap between drug combination-cell line tuples between the three centers, it is not possible to estimate inter-center batch effects, and therefore we must use data from different screening centers separately. The combination benefit is quantified in NCI-ALMANAC by the so-called ComboScore (a modified version of the Bliss independence model). From the entire dose-response matrix of the considered drug combination and cell line tuple, the gain (or loss) of the effect achieved by the combination over the theoretically expected value if the effect was additive is calculated. Positive values of ComboScore indicate a synergistic effect of the combination, whereas the negative correspond to an antagonistic effect (those purely additive obtain a zero ComboScore). Further description of NCI-ALMANAC data is available at Supplementary Information.
(3) MACCS keys encode presence or absence of 166 predetermined substructural fragments as binary vectors (calculated with RDKit). (  4 ) ISIDA fragments encode structure as a vector of numbers of occurrences of substructural fragments of given nature and topology in the molecule  (Varnek et al. , 2005) , which are calculated with ISIDA/Fragmentor  (Ruggiu et al. , 2015). Only one type of fragments is considered here: sequences of atoms and bonds of length 2 to 6 (1,325 features per drug in total). (  5 ) SIRMS fragments are the number of occurrences of 4-atom fragments of varying topology in a molecule, including bonded and nonbonded atoms  (Kuz'min et al. , 2008). Calculated with SiRMS python library (github. com/DrrDom/sirms), it led to 1,454 features per drug. In addition to these sets, 7 physico-chemical features are calculated by RDkit: total polar surface area (TPSA), molecular weight, logP, number of aliphatic and aromatic rings, H-bond donors and acceptors.
Predictive Performance Metrics To evaluate the performance of a model, the following metrics are calculated from observed y obs and predicted y pred ComboScore values: Root Mean Squared Error (RMSE)  and Gillet, 2007 ) RMSE = N y i, obs -y i, pred 2 N Coefficient of determination (R 2 ) (Leach R 2 = 1 - N y i, obs -y i, pred 2 N y i, obs -y obs 2 = 1 - RMSE 2 Var y obs ; y obs = 1 N N i=1 y i,obs Pearson's correlation coefficient (R p ) R p = N (y i, obs -y obs )(y i, pred -y pred ) N (y i, obs -y obs ) 2 N (y i, pred -y pred ) 2 Spearman's rank-order correlation coefficient (R s ) R s = R p (rank y obs , rank y pred ) We use R p between observed and predicted values of ComboScore of a dataset not used to train the model as a primary metric of its accuracy. For proper estimation of the generalization error, these metrics are always calculated here on a test set not used to train or select the model.
Exploratory Modeling of NCI-ALMANAC Data First, we perform an exploratory modeling on the FG datasets in order to determine optimal settings for synergy prediction by assessing various types of features, data augmentation schemes and machine learning methods. The summary of performance improvements is shown on Figure  2. The best median R p across cell lines for RF was obtained with 250 trees, a third of the features evaluated at each tree node, training data augmentation and MFPC fingerprints complemented by physico-chemical properties (256 and 7 features per drug, respectively). The gain of performance with RF is substantial: the median R p increases from 0. 530 (I) to 0. 634 (VI). XGB models are generated with the same features and data set partitions. Changing the machine learning algorithm from RF to XGB does not improve the median test set R p , although both minimum and maximum R p are higher with XGB (boxplots FIGURE 3 | Observed vs.
Performance in Predicting Synergies With Drugs Not Included in NCI-ALMANAC The random data splits that we have used so far may overestimate the model's performance in the case of drug combinations. This would be due to the presence of the two drugs in the combination in both training and test sets, albeit with other partners  (Muratov et al. , 2012). In order to assess to which extent this is the case, we also carry out Leave-One-Drug-Out (LODO) FIGURE 5 | Observed vs. Predicted ComboScore plots for HL-60 leukemia cell line test set (10% of data). Models are built on 90% of FG dataset corresponding to this cell line using RF and XGB methods, both tuned. Each circle is now a drug combination from the entire test set, with its shade of gray indicating one of the three scenarios (as in Figure  4 ).