dataesr/scientific-paragraphs-categorization huggingface dataset:
HK. 02. 02/I/1743/2020) 27 March 2020 4th Edition of Guidelines for Prevention and Control for COVID-19 by the MoH 31 March 2020 Presidential Decree about Large-Scale Social Restrictions due to the Acceleration of COVID-19 Responses (about Indicator of Early Alert and Response Systems in Pandemic Situation 18 May 2020 Online Coordination Meeting about Indicator of Early Alert and Response Systems in Pandemic Situation in five Provinces 7 May 2020 Releasing Surveillance Guidelines for Diseases with Vaccines in the COVID-19 Pandemic Situation 4 June 2020 Training and dissemination of Epidemiologic Surveillance Indicators in Controlling COVID-19 26 June 2020 Training and dissemination of Surveillance Data Application Usage through the New All Record TC-19 June 2020 Procurement of VTM (Viral Transport Medium) and Swabs June 2020 Recruitment of COVID-19 Data Processing Human Resources in five Provinces June 2020 Implementation of Technical Guidance for Monitoring and Evaluation and Validation of COVID-19 Data 13 July 2020 5th Edition of Guidelines for Prevention and Control for COVID-19 by the MoH 15-24 July 2020 Training and dissemination of the 5th Edition of Guidelines for Prevention and Control for COVID-19, and Reinforcement of Daily Reporting System July 2020-Now Implementation of Technical Guidance for Monitoring and Evaluation and Validation of COVID-19 Data, as well as Continuing Evaluation of Early Alert and Response Systems in Pandemic Situation in eight Provinces datasets for ongoing pandemic response decision-making (Ma, 2020; Rybniker & Fätkenheuer, 2020).
This is a crucial and novel big-data capacity development in Indonesia that allows the transparent cross-sectoral review of governmental guidelines and their subsequent implementation within a sectoral context. Three challenges are identified. Firstly, the systems integration of many different data sources; since prior to an integrated national registration system (NAR TC-19), many sub-national governments developed their own systems. After the NAR TC-19, those datasets needed to be extracted and re-integrated into the national system.
Miscellaneous Data Maternal age, parity, outborn birth, gestational age, sex, birth weight, and head circumference at birth were collected. Small for gestational age infants referred to infants with a birth weight strictly less than the 10th percentile according to Fenton growth charts  [22]. Resuscitation actions such as intubation, chest compressions, and epinephrine administration were also reported, along with the ten-minute Apgar score. Although no standardized assessment of the subsequent neurodevelopment was available, infants were classified into three neurological outcome groups from the follow-up reports: (i) normal, (ii) alterations of neurodevelopment but independent walk is present, (iii) severe neurological impairment including cerebral palsy.
Statistical Analysis In the event of normal distribution and variance equality, quantitative variables are expressed as the means (±standard deviation), and Student's t-test for independent samples was used to compare groups in univariate analysis. Concerning variables with nonnormal distribution, the results are expressed as the medians (range), and the Mann-Whitney test was applied. Qualitative variables were compared using the chi-square test. The plasma magnesium kinetics were analysed using t-tests comparing both conditions for each timeperiod. The ability of plasma magnesium to predict the severity of brain injury was assessed using the receiver operating characteristic (ROC) analysis. The comparison of more than two groups of data was performed through one-way ANOVA associated with Bonferroni's post test.
A two-way ANOVA with Bonferroni's post test was performed to explore how plasma magnesium values can be affected by perinatal factors and the MRI results. The significance threshold was strictly less than 5% (p < 0. 05) for all analyses. Statistical analyses were performed using GraphPad Prism statistical software (version 5. 01, San Diego, CA, USA) and MedCalc statistical software (version 19. 1, Ostend, Belgium).
Plasma magnesium peaked at the end of hypoxia before decreasing for the subsequent three hours corresponding to the latent phase. Thereafter, plasma magnesium remained roughly stable during TH, whereas a slight decrease was still observed in the normothermia group  [26]. Therefore, we speculate that an early high plasma magnesium could provide evidence of a recent hypoxic-ischaemic event primarily in infants with WM injury patterns. This hypothesis needs to be confirmed through further prospective data collection. Proton (H1) magnetic resonance spectroscopy (MRS) assesses metabolism through the peaks and ratios of metabolites in specific brain areas. Two metabolites are critical in the perinatal asphyxia context: (i) an increase in lactates reflects brain energy failure, and (ii) a decrease in N-acetyl aspartate (NAA) is associated with injured neurons.
These two phenomena are related to mitochondrial dysfunction and potentially to the cellular balance of magnesium. Interestingly, the lowest plasma magnesium value around the 24th hour of life was associated with the intensity of brain metabolism disturbance in infants with NE  [24]. This association was observed in particular injury patterns, i. e. , concerning the BGT area or multifocal and extensive white matter injuries. These data highlight that plasma magnesium measured around the 24th hour of life may reflect secondary energy failure. This hypothesis is supported by the more profound decrease in plasma magnesium observed during the 12-24 h period in infants with seizures in our cohort. The lack of an unquestionable difference in plasma magnesium values supports the idea that NE is a complex and multifactorial condition  [18].
INTRODUCTION The notion of gene essentiality first emerged as a concept associated with the idea of a minimal genome. [1, 2] A gene is considered essential when a mutation leading to its loss of function or of expression results in a deleterious phenotype, reducing the organism's viability or fitness, leading to the lethality of the organism and/or the population. [3] ne deletions could first identify the effects of these genes in vitro in eukaryotic or prokaryotic models. Then, the effects could be explored in more complex models thanks to systematic deletion, editing or silencing of genes, including prokaryotes,  [4, 5]  flies,  [6]  worms,  [7]  mice  [8]  and human cells in vitro,  [9, 10]  as well as from clinical observations. A 'Database of Essential Genes' (DEG) has been developed to compile these genes in coding as well as noncoding sequences obtained in several prokaryotic and eukaryotic genomes,  [11]  allowing the identification of different essential biological processes across the tree of life.
[88] P3 degrades the extracellular matrix and plays an essential role in remodelling vascular tissue. A polymorphism in the upstream regulatory region of this gene is linked to better arterial elasticity in Europeans, allowing them to experience significantly fewer coronary heart disease events. Since long non-coding RNAs (lncRNAs) are involved in very diverse biological processes and diseases, their essentiality has been studied, first thanks to knockout by CRISPR/Cas9 and knockdown by RNAi and then thanks to new computational methods, databases, and scores. [89] rious methods for predicting the essentiality of microRNAs (miR-NAs) with machine learning also exist,  [90]  showing their critical role in health and disease. Some studies show the link between the essentiality score obtained thanks to algorithms and miRNA conservation,  [91]  but we did not find any relevant reference on the essentiality of noncoding RNA and their variability within species, nor on their implication in the possible variability of sequences 3′-or 5′-UTR or even essential gene enhancers.
The LNH03-6B trial was a phase 3 randomized trial evaluating the efficacy of first-Line R-CHOP delivered every 2 weeks (R-CHOP14) or 3 weeks (R-CHOP21) in diffuse large B-cell lymphoma patients aged 60-80 years with an age-adjusted IPI score greater than or equal to 1 (registered as NCT00144755). We implemented a prospective long-term follow-up (LTFU) program at the end of this trial. The primary endpoints were progression-free survival (PFS) and Overall survival (OS). Relapse patterns and PFS/OS after the first progression (PFS2/OS2) were secondary endpoints. LNH03-6B was registered with ClinicalTrial. gov number NCT00144755. In the LNH03-6B trial, 304 and 296 patients were assigned to receive 8 cycles of R-CHOP14 or R-CHOP21, respectively. LTFU data were investigated for 256/384 (67%) patients who were still alive at the primary analysis.
Study design and patients LNH03-6B was a phase 3, multicenter, randomized trial (NCT00144755) that compared the efficacy of two schedules of immuno-chemotherapy in elderly patients with untreated DLBCL. The study was undertaken at 83 centers in France, Belgium, Switzerland, and Portugal between December 2003 and December 2012. Eligible participants underwent two randomization procedures. In the first, we allocated one of two chemotherapy regimens, R-CHOP14 or R-CHOP21. In the second, we randomly assigned patients to an experimental arm with prophylactic darbepoetin alfa or to a standard arm with conventional "symptomatic" management of chemotherapy-induced anemia. We judged people eligible if they were aged 60-80 years and had untreated DLBCL. Furthermore, patients also needed at least one adverse prognostic factor on the age-adjusted international prognostic index and a good performance status (Eastern Cooperative Oncology Group 0-2).
2012). ] [15] [16]  Long-term follow-up program At the cutoff date of primary analysis of the LNH03-6B trial in December 2012, we implemented a long-term follow-up program in French centers willing to participate in the long-term follow-up program. The inclusion criteria were as follows: patients included in the LNH03-6B trial who were still alive at the end of the trial and were not opposed to long-term data collection. The long-term followup program started at the end of LNH03-6B protocol-specified mandatory follow-up. In this program, the primary endpoints were PFS, as measured from the date of random assignment to either progression or relapse or death from any cause, and overall survival (OS). Secondary endpoints were second progression-free survival (PFS2) and second overall survival (OS2) measured from the date of first progression/relapse for the patients concerned.
During this program, patients' follow-up was assessed according to the habits of each center. We collected status of the disease as judged by the investigator (complete response, partial response, stable disease, progressive disease) at date of last visit or contact. We collected secondary malignancy data and causes of death. We also collected second-line treatment for patients whose disease progressed or relapsed. For this analysis, we distinguished two chemotherapy treatment groups: (i) intensive treatments, considered as "intensive" if usually given in a hospital (in-patient setting) and could usually cause profound cytopenia and other severe side effects, included the following combinations: ifosfamide, carboplatin, etoposide (ICE) and dexamethasone, cytosine arabinoside and either cisplatin, oxaliplatin or carboplatin (DHAX), with or without rituximab, and (ii) non-intensive treatments (usually administered in an outpatient setting) included the following combinations: gemcitabine, oxaliplatin (GEMOX), bendamustine, ifosfamide plus etoposide (IFM-VP16), and different singleagent therapies, with or without rituximab (Supplementary Table  1 ).
Progressions after second-line therapy were captured by collection of "disease status" at date of last contact. The collection of longterm follow-up data was performed via a specific electronic case report form (e-CRF) on a regular basis (at least once a year) and for a minimum period of 10 years for each patient (or less if the patient died or was lost to follow-up). Patients living without progression/relapse or lost to follow-up were censored on their date of last visit or contact.
Patient characteristics Between December 2003 and December 2012, 602 patients were randomized in the LNH03-6B trial in four countries; 600 were included in the intention-to-treat population, 304 were included in the R-CHOP14 group and 296 were included in the R-CHOP21 group. At the time of primary analysis, 384 patients were alive and therefore eligible for the long-term follow-up program. 11 Among these 384 patients, 259 were considered for inclusion in the long-term follow-up database in participating centers. Three patients declined participating in this program (these patients were censored for PFS/OS at the time of consent withdrawal), resulting in a final long-term follow-up sub-population of 256 patients (Figure  1 ). The patient characteristics of the whole population are listed in Table  1.
21, 22 These data were rarely collected in the centers, which seem to comprehensively collect the status of the disease at each visit, but not systematically the data regarding long-term toxicity. To overcome the problem of missing (or potentially miss coded) causes of death, an interesting study would be to estimate the long-term excess mortality hazard in this population as compared to the general population to see how it changes with time and according to prognosis factors. Indeed, in considering the treatment outcomes for a population of patients with a number of competing risks for death and only 3. 7% of relapses beyond 5 years, it would be valuable to investigate long-term DLBCL-specific mortality hazard as compared with the expected mortality hazard in the general population. In addition, the data we collected on the treatments given after the first relapse show the extreme heterogeneity of the indications proposed within a group of investigators used to work together.
In particular, the use of CAR-T cells, whose feasibility and target population is wider than those of ASCT, Downloaded from http://ashpublications. org/bloodadvances/article-pdf/doi/10. 1182/bloodadvances. 2022007609/1904433/bloodadvances. 2022007609. pdf by guest on 05 July 2022 appears promising in the management of relapsed elderly DLBCL patients. 9,10 With this finding in mind, the LYSA is currently studying axicabtagene ciloleucel as a second-line therapy in patients with relapsed/refractory DLBCL who are ineligible for ASCT (ALYCANTE trial, NCT04531046).
developed an AI algorithm for differentiating between the following six nodule types: spiculated, solid, partsolid, non-solid, calcified, and perifissural  [23]. The training of the DL system was carried out with data from the Italian MILD screening trial and the validation performed on an independent set of data from the Danish LCS trial, which was also assessed by four independent radiologists. The results showed that the performance of the DL algorithm was within the limits defined by the inter-observer variability in the four experienced readers, thus performing equally to an independent human expert. With the knowledge that large nodule size is one of the best predictors of malignancy, it can be determined by manually measuring the longest and perpendicular diameters in the transverse plane.
developed an AI algorithm for differentiating between the following six nodule types: spiculated, solid, part-solid, non-solid, calcified, and perifissural  [23]. The training of the DL system was carried out with data from the Italian MILD screening trial and the validation performed on an independent set of data from the Danish LCS trial, which was also assessed by four independent radiologists. The results showed that the performance of the DL algorithm was within the limits defined by the inter-observer variability in the four experienced readers, thus performing equally to an independent human expert. With the knowledge that large nodule size is one of the best predictors of malignancy, it can be determined by manually measuring the longest and perpendicular diameters in the transverse plane.
Despite many improvements in screening procedures using AI-based solutions to detect and classify pulmonary nodules, the performances of the developed algorithms need to be proven to be quite robust in external datasets before being implemented in routine clinical care. Diagnostics 2022, 12, 2435 4 of 13 that both CAD-based mean diameter and volume models were able to determine nodule malignancy risk in a similar way to the radiologist-based model  [29]. Despite many improvements in screening procedures using AI-based solutions to detect and classify pulmonary nodules, the performances of the developed algorithms need to be proven to be quite robust in external datasets before being implemented in routine clinical care. table that reports the lobe location, 2D and 3D diameters, and volume of the 3 detected nodules.
Malignant Prediction Accurate estimation of the malignancy risk of pulmonary nodules is essential and remains challenging. In practice, to predict the malignancy of a pulmonary nodule, radiologists currently use the statistical risk model established using patient demographics, nodule size, type and morphology. The most widely used statistical risk model for estimating nodule malignancy risk is the Brock model, also known as the PanCan model  [30]. Nevertheless, accurately distinguishing between benign and malignant nodules remains a challenge. Several studies  [31, 32]  showed the potential of DL with convolution neural networks (CNNs) in predicting the malignancy risk of a pulmonary nodule through the publicly available Lung Image Database Consortium image collection data set  [33]. However, the algorithm performances were evaluated by comparison with the subjective scoring provided by radiologists without a definitive reference standard for malignant and benign nodules.
On the other hand, for nodules detected during incidence screening, their growth and appearance on the previous CT examinations are important to consider. As nodule growth on CT is the most important predictor of cancer, in order to estimate lung cancer risk, Ardila et al. developed a DL system mainly based on changes in nodule volume  [36]. They trained and tested the algorithm on data from 42,290 and 6716 NLST participants, respectively, and validated it retrospectively in an independent clinical dataset of 1139 individuals. In the test set, the model achieved an AUC of 94. 4% (95% CI 91. 1-97. 3%), with a similar result obtained in the external validation set. When multiple scans were available, the model performance was equal to that of radiologists. Huang et al. developed a DL algorithm to identify nodule features that were predictive of malignancy on the screening chest LDCTs within a three-year period  [37].
The training set included baseline and follow-up LDCT data from 25,097 NLST participants who had undergone at least 2 LDCT scans. The validation set included LDCT data of 2294 participants from the Pan-Can study. Performance of the AI algorithm score to inform lung cancer incidence was compared with Lung-RADS and volume doubling time. Compared to the Pan-Can model incorporated into Lung-RADS, the algorithm classified a high-risk group that was smaller and had a higher proportion of cancers. Individuals with high AI scores had significantly higher mortality rates compared to those at lower risk. The algorithm also identified more accurately those with very low risks of lung cancer within 2 years. Although these studies show promising results, several additional important steps need to be finalized before the AI algorithms can be widely accepted into screening practice.
More specifically, Chao et al. trained a DL cardiovascular disease risk prediction model with 30,286 LDCT scans from the NLST  [48]. They achieved an AUC of 0. 871 on a separate test set of 2085 subjects and identified patients with high cardiovascular mortality risks (AUC of 0. 768). Then, the same investigators validated their model against ECG-gated cardiac CT-based markers obtained from an independent dataset of 335 subjects. Hence, the potential of obtaining a quantitative and reliable cardiovascular disease risk score by analyzing the same scans as those for LCS may benefit a large patient population. In addition, the automatic exclusion of LDCT scans with a negative test for CAC could significantly reduce the workload of radiologists  [49]. In 2020, Lee et al. summarized the current AI-based applications for CAC scoring and their potential clinical impact  [46].
Recent developments of DL-based algorithms have provided considerable progress in CAC evaluation. Several investigations that evaluated the clinical role of DL solutions in CAC assessment showed excellent agreement between those algorithms and manual scoring  [46, 47]. More specifically, Chao et al. trained a DL cardiovascular disease risk prediction model with 30,286 LDCT scans from the NLST  [48]. They achieved an AUC of 0. 871 on a separate test set of 2085 subjects and identified patients with high cardiovascular mortality risks (AUC of 0. 768). Then, the same investigators validated their model against ECG-gated cardiac CT-based markers obtained from an independent dataset of 335 subjects. Hence, the potential of obtaining a quantitative and reliable cardiovascular disease risk score by analyzing the same scans as those for LCS may benefit a large patient population.
DL-based solutions have been developed to detect and quantify emphysema on LDCT examinations  [55] [56] [57]  (Figure  1 ). In practice, lung abnormalities are visually assessed using high-contrast thin-slice images reconstructed from raw scan data using sharp kernels, despite increased image noise. On the opposite side, accurate CT quantification requires low-contrast thin-slice images with low noise, which are reconstructed with soft kernels. Investigators have applied DL techniques for converting sharp-kernel images to soft-kernellike images and normalizing CT kernels effects in order to reduce the kernel-induced variability in lung density measurements  [56, 57]. Hence, the DL algorithm has the potential to increase the accuracy of emphysema quantification, and can allow reliable surveillance of emphysema in LCS, even if follow-up CT scans are acquired with different reconstruction kernels.
Then, the mean CT numbers of the trabecular area of target vertebral bodies were obtained based on the segmentation mask, and mapped with their bone mineral densities collected from approved software used for osteoporosis diagnosis. The authors evaluated the diagnostic performance of the developed system using an independent dataset of 374 LDCT scans with standard bone mineral densities and osteoporosis diagnosis. The DL algorithm achieved 86. 6% and 97. 5% accuracies for vertebral body segmentation and labeling, respectively. Linear regression and Bland-Altman analyses showed good agreement between the predicted bone mineral measure and the ground truth, with correlation coefficients of 0. 964-0. 968 and mean errors of 2. 2-4. 0 mg/cm 3. The sensitivity and specificity of the developed system for detecting osteoporosis were 85.
7% and 99. 7%, respectively, with an AUC of 0. 927. These diagnostic performances seem to be quite promising for automatic measurement of vertebral bone mineral density in opportunistic osteoporosis screening, using LDCT scans obtained for LCS (Figure  4 ). with 200 annotated LDCT scans to segment and label all vertebral bodies. Then, the mean CT numbers of the trabecular area of target vertebral bodies were obtained based on the segmentation mask, and mapped with their bone mineral densities collected from approved software used for osteoporosis diagnosis. The authors evaluated the diagnostic performance of the developed system using an independent dataset of 374 LDCT scans with standard bone mineral densities and osteoporosis diagnosis. The DL algorithm achieved 86. 6% and 97. 5% accuracies for vertebral body segmentation and labeling, respectively.