dataesr/scientific-paragraphs-categorization huggingface dataset:
Background: Patients with mild asthma represent a substantial proportion of the population with asthma, yet there are limited data on their true burden of disease. We aimed to describe the clinical and healthcare resource utilisation (HCRU) burden of physician-assessed mild asthma. Methods: Patients with mild asthma were included from the NOVEL observational longiTudinal studY (NOVELTY; NCT02760329), a global, 3-year, real-world prospective study of patients with asthma and/or chronic obstructive pulmonary disease from community practice (specialised and primary care). Diagnosis and severity were based on physician discretion. Clinical burden included physician-reported exacerbations and patientreported measures. HCRU included inpatient and outpatient visits. Results: Overall, 2004 patients with mild asthma were included; 22.
The NOVEL observational longiTudinal studY (NOVELTY; NCT02760329) is a multinational (19 countries across the Americas, Asia, Australia and Europe), prospective, observational study of 11,243 patients with a physician-assigned diagnosis or suspected diagnosis of asthma and/or chronic obstructive pulmonary disease (COPD). Using baseline data from NOVELTY, this analysis aimed to describe the clinical and healthcare resource utilisation burden of physician-assessed mild asthma, using both physician-reported and patient-reported measures.
NOVELTY study design Details of the NOVELTY study design  [14]  have been published previously. Enrolment was stratified by physician-assigned diagnosis (asthma, COPD or both asthma and COPD [asthma+COPD]) and physician-assessed severity (mild, moderate, or severe), to ensure sufficient patient numbers for sub-group analyses. No criteria were provided for the diagnosis of asthma or COPD, nor for the assessment of severity; instead, both diagnostic and severity criteria were left at the discretion of the managing physician to capture standards of care at the community level. Physicians were not aware of data from patient-reported questionnaires when assessing disease severity. A minimum period of 6 weeks was required between any exacerbation and the baseline visit. Data for patients from China were not included in the analysis due to a change in regulations about data transfer in May 2019, as were data from sites violating eligibility criteria.
Patients Details of the NOVELTY patient population, including some data for patients with physician-assessed mild asthma, have been described previously  [15]. This pre-defined, cross-sectional analysis of baseline data includes patients with physician-assessed mild asthma with available exacerbation outcome and medication data; patients with physician-assessed moderate or severe asthma, or a physician-assigned label of COPD or asthma+COPD, were excluded from this analysis.
Hours of work lost due to health problems in the previous week were reported as the proportion of hours worked that week. Other questions included the number and proportion of patients with one or more patient-reported episode of symptomatic worsening in the past 3 months (defined as a worsening of the patient's breathing beyond what they experienced in a typical day), and the proportion of these treated with OCS. It was not appropriate to compare physician-reported exacerbations and patient-reported episodes of symptomatic worsening, due to different definitions and recall periods used in the study. Healthcare resource utilisation burden was described as patient medication usage and the number and proportion of patients with one or Data were also collected for hospital admissions not related to respiratory disease.
Physician-reported exacerbations In total, 1997 patients with physician-assessed mild asthma (99. 7%) had data available for physician-reported exacerbations. Among these, 455 (22. 8%) patients experienced one or more physician-reported exacerbation in the previous 12 months (Fig. 1A ), corresponding to an annualised exacerbation rate of 0. 3 (SD ± 0. 8, range 0-16) (Table  1 ). Of patients who experienced exacerbations, 329 (72. 3%) had one or more severe exacerbation, with an annualised rate of 0. 2 (SD ± 0. 6, range 0-5) (Table  1 ). During the 12 months prior to baseline, 9. 5% of patients had one or more exacerbation-related hospital or clinic visit (including nonscheduled emergency visits), 9. 2% had one or more emergency department visit and 1. 1% had one or more hospital admission due to exacerbations (Fig.
However, the cross-sectional study defined mild asthma using GINA treatment steps  [25]  (which is only recommended for epidemiologic studies, where other patient data are not available), reflecting patients' current prescriptions, rather than physician assessment of severity. When we restricted patients in the physician-assessed mild asthma subgroup to those who were on GINA 2017 treatment steps 1 and 2, the resulting sample had broadly similar clinical characteristics to the overall physician-assessed mild asthma cohort. Likewise, few differences were observed between the overall physician-assessed mild asthma cohort and all NOVELTY patients with physician-assigned asthma on GINA 2017 treatment steps 1 and 2, with the proportion of patients who had an exacerbation in the previous 12 months being similar between the two patient groups.
Asthma severity was physician-assessed in order to understand the characteristics and burden of patients judged by clinicians as having mild asthma. While the NOVELTY population as a whole is not representative of asthma prevalence (due to recruitment being stratified by severity), this limitation does not apply within the mild asthma population studied here. Limitations of this analysis include delay of the baseline assessment if the patient had experienced a recent exacerbation until at least 6 weeks after resolution, and the recruitment of patients from clinical practice potentially leading to biased selection of patients making frequent healthcare visits  [14]. Data were missing for some measures, notably for details of individual events of physician-reported exacerbations and related treatments.
Acknowledgements The NOVELTY study is funded by  AstraZeneca (AZ). The authors wish to acknowledge the work of the NOVELTY study investigators, who are listed in full in Supplementary Table 7. We thank the late  Professor Mark FitzGerald  for his contributions to the NOVELTY study and his authorship on the manuscript, and express our condolences to his family, friends and colleagues. The authors also thank  Richard J Martin  ( National Jewish Health and the University of Colorado, Denver, USA ) for his contribution to the NOVELTY study design and interpretation of data as a member of the  NOVELTY Scientific Committee. Medical writing support, under the direction of the authors, was provided by  Lauren Hogarth ,  MSc , and  Niall Tyrer ,  MBiolSci ,  CMC  Connect, McCann Health Medical Communications, and was funded by  AZ , in accordance with Good Publication Practice (GPP3) guidelines (Ann Intern Med 2015; 163: 461-4 ).
Similarly, Kommalapati et al. (33) , in a retrospective study including more than 3000 cases of rSCC (all stages) from the SEER database between 2004 and 2015, showed an increase in OS for stages II and III: median OS increased from 76 months for patients treated with surgery alone to 108 months for those treated with CRT (p=0. 012). These data were not significant for stage I patients. In order to better define the tumor response rates expected following CRT, we reviewed twelve case series reported in the literature, including a total of 103 patients treated by CRT for a localized or locally advanced rSCC (Table  3 ; 13, 34-44). An objective response rate (ORR) of 93% was reported after CRT for all stages and 74 out of the 103 assessable patients achieved a complete response (72%). The ORR was 95% for stage III (39 patients out of 41 assessable patients) and the complete response rate (CRR) was 57% (24 out of 42 assessable patients).
Patients All consecutive patients with histologically proven rSCC who were treated with mDCF chemotherapy in two French hospitals (University Hospital of Besanc ̧on and North Franche-Comte Hospital) between January 2014 and December 2021 were included in this study. Patients with tumors involving the anal canal or the anorectal junction were excluded. All women underwent gynecologic examination to exclude a primary gynecologic tumor. Demographics, cancer history, pathological, clinical, biological, and radiological (tumor response according to Response Evaluation Criteria in Solid Tumors [RECIST] v1. 1 criteria) parameters at the beginning of mDCF treatment, as well as treatment outcomes, were retrospectively collected from medical records. The database was locked on 04/29/2021. Immunohistochemistry staining (p53, p63, p40, CK20, CK7, CK8, CDX2, SATB2) was completed for the available samples.
Statistical analysis Median value (interquartile range [IQR]) and frequency (percentage) were provided for the description of continuous and categorical variables, respectively. OS was calculated from the date of diagnosis to the date of death from any cause. Survival data were censored at the last follow-up. Recurrencefree survival (RFS) was calculated from the date of the end of specific treatments to the date of recurrence or death from any cause, or the date of the last follow-up, at which point data were censored. OS and RFS were estimated using the Kaplan-Meier method and described using median or rate at specific time points with 95% confidence intervals (CI), and compared using the log-rank test. ORR and CRR were determined according to RECIST v1. 1 criteria. Toxicity was evaluated according to the National Cancer Institute Common Terminology Criteria (CTCAE v5).
In Africa, grassroots NLP research communities involving academic and non-academic actors have the potential to allow greater scientific impact and local engagement. They can also contribute to building richer and more meaningful NLP datasets that detect embedded biases and stereotypes, while including the priorities of stakeholders. Much must be done to develop a rigorous theoretical approach toward understanding how the use of global languages and narratives affects sustainability transitions in Africa. The multilingualisation of sustainability(ies) is an intersectional challenge that can only be dealt with through interdisciplinary and transdisciplinary approaches. The integration of different disciplines-e. g. , social psychology, cognitive ergonomics, environmental sciences, sociolinguistics, and artificial intelligence's natural language processing (NLP)-can collaboratively contribute to shed new light on this and on several academic fields of study in which sustainable development remains a contested issue.
Design and setting This is a prospective diagnostic accuracy study to evaluate the HAST at LA (760m) to predict ARAHE in COPD patients during a sojourn of 2 days/nights at HA (3100m). We nested the study within a trial on self-monitoring of COPD-patients to detect early signs of ARAHE whilst traveling to HA conducted May-July 2021 at the National Center for Cardiology and Internal Medicine, Bishkek, and the HA-clinic Too Ashu, Kyrgyzstan. Participants provided written informed consent and the protocol was approved by the Ethics Committee in Bishkek (01-7/179), endorsed by the Ethics Committee Zurich and registered at clinicaltrial. gov (NCT04915378).
United Kingdom: association of British clinical diabetologist (ABCD) COVID-19 audit ABCD COVID-19 audit collects data retrospectively from hospitals across the UK. Clinicians submit data for adults with preexisting type 1 and type 2 diabetes infected with COVID-19. The audit is registered with Oxford University Hospitals NHS Foundation Trust (OUH) and a Data Protection Impact Assessment was carried out and approved by the OUH Caldicott Guardian and the Public Benefit and Privacy Panel in Scotland (reference 2021e0111). The NHS supports audit with clear guidance for the contributing centers on the use of routine clinical practice data submitted in anonymized form via the secure NHS network  [12].
Spain The six hospitals in the HM Hospitales group collected anonymized observational data for people infected with COVID-19 during the first wave. This dataset is made available to researchers via "Covid Data Save Lives"  [9]. The electronic hospital health records were collected for admitted persons including pre-existing disease status, medication use, demographic and outcome. A subset of people with pre-existing diabetes from this cross-sectional database was used in this study. Before access was granted, a formal petition, specific study protocol, and ethics committee approval were obtained. The study was approved by the Ethics Committee of the Primary Health Care University Research Institute (IDIAP) Jordi Gol, Barcelona (approval number: 20/089-PCV).
Data collection: definitions and outcomes Demographic data included: age, gender and type of diabetes. UK and France collected ethnicity data (White/Europid, Black/African, Asian/Asian, Other/Middle East and North African (MENA)). Medication use at point of admission was collected with particular focus on those medications associated with diabetes or diabetesrelated comorbidities. This study focused on statin, ACEi and ARB use. Microvascular disease (including retinopathy, neuropathy and nephropathy) was collected for the French and UK cohorts. The Spanish cohort collected presence of chronic kidney disease (CKD) alone based on clinical coding record. CKD was defined by eGFR <60 ml/min or presence of macroalbuminuria. To improve homogeneity of the data and reduce collinearity we reported on CKD only.
Statistical analysis We conducted complete-case analyses and reported descriptive statistics comparing the complete dataset to those excluded due to missing variables. Clinical characteristics were reported as frequency and percentages for categorical variables, and as mean and standard deviation for continuous variables. Multivariable logistic regression models were used to obtain odds ratios for the outcome variable, death. The main exposure was RAASi use, statin use, or both, and associations were estimated using as a reference category neither RAASi nor Statin. Potential confounders were ethnicity, CKD, macrovascular complications, hypertension, gender and type of diabetes (categorical) and age (continuous). Logistic regressions were performed using R in each contributing country; countryspecific odds ratios were then pooled in a random-effects DerSimonian and Laird meta-analysis, with estimates stratified by model adjustment.
Results The UK ABCD COVID-19 audit collected data on 3007 people with diabetes from twenty-five hospitals between March and October 2020. Of these 1849 individuals (61. 4%) had complete data required for this study and were included in this analysis. CORO-NADO investigators collected data on 2843 people from 68 hospitals with 1219 (43%) having complete data set. Spanish investigators collected data on 2310 individuals at six hospital sites, the majority without hyperglycaemia or diabetes. There was complete data for 406 Spanish individuals (18%) with pre-existing diabetes. Comparison of the complete dataset to that with missing data showed no difference in mean age, gender, ethnicity or type of diabetes across all three countries (Supplementary Material Table  1 ). Ethnicity data were not available for the Spanish cohort.
The final dataset included 3474 individuals with diabetes and COVID-19 infection. One thousand-and nineteen (29. 3%) people died. Characteristics of the study populations including prescribing patterns and mortality by country are shown in Table  1. Output from individual logistic regression models for each country were combined in a meta-analysis and showed no significant association between death and use of either RAASi or statin therapy with considerable heterogeneity (Fig. 1 ). The overall odds ratio for RAASi use and mortality was 1. 09 after adjusting for age, gender, type of diabetes, presence of macrovascular disease, CKD and hypertension and ethnicity (model 4), however this did not reach significance (CI 0. 78e1. 52) with little evidence of heterogeneity (I 2 22. 2%). Similar non-significant association was seen for statin use (OR 0.
This paper specifically examined medications used by many people with diabetes (type 1 and 2) for the prevention of diabetic complications and, therefore, the potential detrimental or beneficial effect of these medications on outcomes for this specific population. It uses large heterogenous real world datasets to draw clinically relevant conclusions. Early use of statin therapy is globally advocated to prevent diabetes complications however the proportions receiving statin prescriptions in our study cohort varied between 26% in Spain and 61% in the UK  [8]. Current guidance does not advise stopping statin therapy during intercurrent illness. Prior to the COVID-19 pandemic, secondary analysis of a randomised, placebo-controlled trial in acute respiratory distress syndrome (ARDS) showed improved survival with simvastatin treatment in a sub-group of participants with hyperinflammatory state  [13].
It provides reassurance about continued outpatient use of RAASi and statin therapy during the pandemic, with no significant association with increased mortality or disease severity in those prescribed them. The multinational and demographically diverse datasets allow for wider generalisability of findings. Legend: Odds ratio of death associated with use of either renin-angiotensin-aldosterone system inhibitor (RAASi)(indicated as ACEi/ARB), Statin or combined therapy in each country and overall (random-effects meta-analysis). I 2 indicates heterogeneity in the estimates. The baseline level for odds ratio estimates is the category "neither on ACEi/ARB nor on Statin". Odds Ratio estimates adjusted for. Model   Our study is limited by the heterogeneity of data collection methods across the nations due to use of databases not initially created in collaboration to answer the study question and, therefore, by some missing data.
The meta-analysis combines a larger dataset to provide an overall relationship of association, rather than causality, but masks heterogeneity across nations. Markers of inflammation and lipid levels were not collected, as well as other potential confounding factors such as smoking status, body mass index and diabetes duration. Lastly, drug histories were often collected from health records, rather than being confirmed with individuals, introducing the possibility of error in the context of access to pharmacies being restricted during pandemic lockdowns.
Acknowledgments The contributors to national studies are listed in supplementary material. With regard to the CORONADO initiative, we thank the sponsor ( DRCI, Nantes University Hospital ),  Clinical Project Manager (Ma€ eva Saignes ) and assistant ( Jeanne Saunier ),  Clinical Research Associates  ( Selma El Andaloussi ,  Jo€ elle Martin-Gauthier ,  Emily Rebouilleau ) and data manager (Tanguy Roman). We thank the  Communication Manager of l'Institut du Thorax (Vimla Mayoura ). We acknowledge all medical staff involved in the diagnosis and treatment of patients with COVID-19 in participating centers. We thank all GPs, specialists, pharmacists and biological laboratories in charge of hospitalized patients for providing additional medical information to our investigators. We thank the  Soci et e Francophone du Diab ete (SFD)  and  Soci et e Française d'Endocrinologie (SFE)  for disseminating study design and organization, the  F ed eration Française des Diab etiques (FFD)  for participating in the organization of the study.
However, if mNGS is associated with the prospection of the lung resistome, some issues still need to be settled. mNGS data analyses rely on databases that do not necessarily reflect the exhaustive panel of ARGs, only the reference genes, limiting the interpretation. Comparisons based on similarities with reference ARGs allow the detection of known ARGs or variants but can rarely predict totally new ARGs. Also, some resistance predictions arise from specific gene mutations, which are more challenging to analyse, even if known chromosomal mutations conferring resistance can be detected  [56]. Thus, although the prediction of phenotypic resistance based on mNGS results seems a tempting prospect, some false-negative results may still occur when compared with phenotypic methods  [57]. In addition, the interrelationship between ARGs predicted by mNGS and the resistance phenotype may be unclear.
It has been shown that some putative ARGs fail to confer resistance when expressed, such as the putative aminoglycoside kinases Rv3817 and Rv325c from Mycobacterium tuberculosis when expressed in Escherichia coli  [12]. Some technical concerns also have to be accounted for when using mNGS, e. g. the high proportion of human DNA in the respiratory tract complicating the extraction of microbial sequences, and thus ARGs  [1]. The relatively low abundance of ARGs also requires an adequate sequencing depth for an exhaustive and meaningful assessment of the respiratory resistome  [58]. Another restriction is the high price of mNGS if applied to large patient cohorts, which may impede routine application for antibiotic stewardship. To increase the sensitivity of resistome analysis, some studies propose ARG enrichment using a targeted capture method with a set of probes designed from reference sequences of the Comprehensive Antibiotic Resistance Database  [59].
Three-dimensional modelling Other functional predictive approaches could be adapted from the exploration of the intestinal resistome to predict ARGs from metagenomic datasets. RUPPÉ et al. [64]  developed a method based on protein homology modelling named "pairwise comparative modelling". This method increases the functional prediction of proteins by building structural models and enables the classification of predicted antibiotic-resistance determinants. These can then be assigned to bacterial phyla and located and investigated for their mobility potential, distribution in the human microbiome, and dynamics within the antibiotic exposure being investigated  [64]. This approach complements metagenomic analysis with the functional prediction of ARGs. Furthermore, three-dimensional modelling helps predict the harmful consequences of specific mutations in the reference protein  [56].
Balancing food security, ecosystem functioning and other socio-ecological goals The ecological management of multispecies fisheries on tropical reefs is challenging because fisheries and landing sites are widely dispersed, catches are often unreported  80  and formal governance, monitoring and management are often lacking  81. However, the need for sustainable reef fisheries has motivated the search for solutions through alternative fisheries-independent, biomass-based models, showing that fishing can be compatible with the maintenance of key ecosystem functions  82. These approaches have typically aggregated fish communities into either total biomass  82  or trophic group biomass  12  and used space-for-time substitutions of large observational datasets to infer temporal dynamics. Consistently, such work has shown that, out of a total unfished biomass averaging ~1,000 kg ha -1 (ref.
These multispecies models will need to account for future changes in fish nutritional content as well as food safety parameters that, while beyond the scope of this paper, are likely to change with, for example, increasing levels of methylmercury in warmer oceans  86. A critical first step will be to collate comprehensive food composition and safety databases from small-scale domestic fisheries, along with country-specific statistics on catches, local consumption and consumer preferences (Table  1 ), which are still crucially lacking in many countries with coral reefs. Temperature-dependent models 87 that also account for species range shifts  88  and life history traits  89  will then be needed to project future changes in fisheries contributions to food and nutrition security, accounting for associated uncertainty (Box 2).