dataesr/scientific-paragraphs-categorization huggingface dataset:
A second reason is that we found, with regard to the overall content of biosecurity assessment checklists that some key aspects of farm biosecurity were at times missing. For example, checklist items on water quality and sanitation were absent in 8/20 databases, despite the importance of water in fecal-oral contaminations  (28, 29). Checklist items on vehicle sharing also were absent in 7/20 databases, despite the known risk of farm-to-farm contamination during avian influenza and infectious laryngotracheitis epidemics  (17, 30). The same remarks apply to the conditions of poultry unloading  (31) , the absence of an all-in/all-out system  (32, 33) , and vehicle flow management  (33, 34). In this study, some aspects of on-farm biosecurity were described as being present in most databases, but this does not mean that they were assessed in a reliable manner.
For example, carcass management, assessed in 19/20 databases, may rely solely on asking farmers whether they pick up carcasses daily (35) rather than addressing the conditions under which the carcasses are brought to the storage room  (36). Collecting and analyzing the original checklists would help identify more precisely which biosecurity items may be missing from biosecurity assessment systems. However, we need to keep in mind that some differences in biosecurity checklists were to be expected due to the specific characteristics of certain production stages. Egg management procedures, reported in 12/20 databases, are obviously specific to egg-layer production systems (including poultry breeders). With regard to the conditions of data collection, we should keep in mind that repeating similar biosecurity assessments on farms may confuse or annoy farmers.
In addition, the cost associated with unnecessary campaigns of on-farm biosecurity assessment must be considered for various stakeholders. In France, for poultry farms, the veterinary authorities are now validating biosecurity assessments conducted in the framework of industry certifications and controls. In Ireland, the veterinary authorities also fund free biosecurity assessments once a year for poultry and pig farmers, using a checklist of academic origin. The last part of database construction concerns entering the biosecurity assessment data in the database, that is to say digitally. While the people in charge of biosecurity assessment may find it more convenient to collect data on paper, it takes additional time to input the data into the database. In two cases, biosecurity data were collected but not entered into any database.
At the level of database access and analysis, several aspects require discussion. First, this study highlights the challenges associated with data sharing on biosecurity assessment. Some reasons are linked to database owners' strategies and prospects, or relations and contracts between organizations. Sharing biosecurity databases offers opportunities to implement different types of analysis strategies, ranging from transversal analysis across different countries to connection with other types of data (health, production, etc. ) in order to document the relationship between biosecurity characteristics and various outcomes. Sharing and connecting biosecurity compliance databases offers the possibility to compare biosecurity practices between different poultry production species, farming systems, and countries.
Nevertheless, a comparison of biosecurity compliance databases could provide-to a certain extent-a useful benchmarking tool. In addition, it could be used to highlight which practices may be more easily adopted regardless of the context. Similarly, it could help identify common challenges and the measures that have been successful in addressing them, providing lessons learned to benefit others. Another key aspect of biosecurity database access and analysis is the possibility to produce knowledge on the benefits of biosecurity. The main challenge here lies in allowing research organizations to use the databases. One of the main barriers cited by farmers for the implementation of biosecurity measures is the lack of available evidence on the benefits of biosecurity on animal health, antibiotic use and animal productivity  (38) (39) (40).
Previous works have used existing biosecurity databases to prove the benefits of biosecurity. For example, a study pointed out the main biosecurity risk factors associated with avian influenza infection in poultry farms, using a database of biosecurity inspections managed by the French veterinary health services  (34). Similarly, accessing various datasets owned by producer organizations (economic performance and biosecurity audits) made it possible to assess risk factors of contamination by Campylobacter  (41). Slaughterhouse databases  (42, 43)  and welfare control databases (44) also have proved to be extremely useful in efforts to address similar changes. In such cases, it is a win-win situation, where database owners may profit from data they collected for purposes others than research and that they did not plan to analyze themselves.
Finally, concerning database analysis, a crucial aspect involves how feedback is provided, which could be at the farmer level or more widely to a broader audience. The farmer level feedback reported in this study mostly involved personalized reports. This is not surprising since certification, advice, and enforcement of regulations aim to present results directly to the farmer, with the ultimate objective being to improve biosecurity compliance on the farm. It also constitutes a first step to propose farm-tailored advice. Research shows that to achieve change in animal health related behavior on a farm, advice must be tailored to the characteristics and needs of that farm  (45) (46) (47). Interestingly, some feedback to farmers may include a benchmark that farmers can use to compare their own biosecurity practices with those of other farmers.
This is also an important tool to modify behavior, as farmers may (1) be sensitive to being compared to others (social norm) and (  2 ) require proof that they can realistically adopt biosecurity practices on a routine basis (the concept of auto-efficacy)  (48, 49). For this purpose, the results of a biosecurity assessment must be directly or at least promptly entered into a digital database so that a comparison between the farmer and the general population of farmers who use that assessment tool can be made. An efficient way to present such comparisons is to produce radar plots  (9). Smartphone apps and web-based tools may offer the possibility to enter the results easily and obtain feedback instantly. Feedback also may be considered at a broader level in the form of reports presenting analyzed data.
These reports can be communicated to concerned farmers but also to others, and serve any stakeholder seeking to assess the overall evolution of biosecurity practices. Such reports can take the form of scientific articles or open access reports by veterinary health services. Based on all of these challenges and opportunities related to the creation, maintenance, and use of biosecurity databases, we have developed a set of recommendations. First, establishing an atlas of biosecurity databases would help all stakeholders have an overview of when, where and under what conditions biosecurity is assessed on poultry farms. This would be crucial to avoid unnecessary biosecurity assessments and would also create opportunities for data analysis by researchers. The same concept has been applied to veterinary antibiotic stewardship at the European level  (50).
Second, we suggest that standardizing some aspects of biosecurity assessment would help to avoid missing key biosecurity items and to exploit databases more easily and efficiently (feedback, specific reports, transversal reports, and provision of knowledge on the effects of biosecurity). This is one of the objectives behind the deployment of a standardized biosecurity assessment tool which now takes into account the specificities of different poultry production types  (51). Even if all stakeholders do not agree on using the same assessment tool, a checklist of essential biosecurity items could be established and disseminated. Finally, we suggest that some meta-data should be collected on farm types, with respect to the European Union General Data Protection Regulation. This meta-data would include, for example, farmed species (boiler/layer chicken, turkey, waterfowl, etc.
), production type (intensive, free-range), farm size (in terms of barn capacity or annual production), and whether farms market their products through short supply chains. While this study was conducted in seven countries and only in the poultry industry, the results presented here and the general concepts behind the use of biosecurity databases may be transposed to other countries or farmed species for which the control of biosecurity is of major importance. In some countries, databases on biosecurity implementation are still absent and the organizations in charge of implementing disease control strategies may take these results into consideration. In conclusion, although they are often related to the enforcement of biosecurity regulations, biosecurity assessment systems, and their databases form a heterogeneous whole.
Acknowledgments The authors wish to thank all participants for taking this survey. For data collection in Hungary, the authors wish to thank  Mária Kovács-Weber  ( Department of Animal Husbandry Technology and Welfare, Institute of Animal Breeding Sciences Hungarian University of Agriculture and Life Sciences, Hungary ),  László Könyves  ( Department of Animal Hygiene, Herd Health and Mobile Clinic, University of Veterinary Medicine, Budapest, Hungary )  András Székács  ( Department of Environmental Analysis and Technologies, Hungarian University of Agriculture and Life Sciences, Hungary ), and  Miklós Süth  ( Digital Food Chain Education, Research, Development and Innovation Institute, University of Veterinary Medicine, Budapest, Hungary ).
Introduction Psoriasis is a chronic inflammatory skin disease. Adalimumab is an effective but previously expensive biological treatment for psoriasis. The introduction of biosimilars following the patent expiry of the originator adalimumab Humira has reduced the unit cost of treatment. However, the long-term effectiveness and safety of adalimumab biosimilars for treating psoriasis in real-world settings are uncertain and may be a barrier to widespread usage. Methods and analysis This study aims to compare the drug survival and safety of adalimumab biosimilars to adalimumab originator for the treatment of psoriasis. We will use both routinely collected healthcare databases and dedicated pharmacovigilance registries from the PsoNet initiative, including data from the UK, France and Spain. We will conduct a cohort study using a prevalent new user design.
Data sources This study comprises routinely collected healthcare data from the French Système National des Données de Santé (SNDS) and data from dedicated pharmacovigilance registries from the PsoNet network. PsoNet is composed of independent pharmacovigilance registries and healthcare databases focused on assessing the safety of biological therapy for patients with moderateto-severe psoriasis. Two PsoNet studies provided data for this study: The British Association of Dermatologists Biologics and Immunomodulators Register (BADBIR) and the Spanish Registry of Systemic Therapy in Psoriasis (Biobadaderm). BADBIR and Biobadaderm have been previously described. 17 18  Briefly, BADBIR and Biobadaderm are longitudinal, multicentre, observational registers of patients with moderate-to-severe psoriasis who are receiving either conventional systemic or biological therapies for psoriasis.
Interventions and comparators The study interventions are initiating adalimumab treatment with biosimilars, including Amjevita/Amgevita/ Solymbic, Hyrimoz/Hefiya/Halimatoz, Idacio, Imraldi; or non-medical switching from adalimumab originator to adalimumab biosimilars. The comparators are treatments with adalimumab originator Humira. In registry databases, we define a starter as a patient who has not been previously exposed to adalimumab (known as adalimumab-naïve) and has started their first treatment with either originator or biosimilar adalimumab. A switcher is defined as a patient who has used adalimumab originator and then switched to adalimumab biosimilar, with at least one follow-up visit after the switch. In the claims database, a starter is defined as a patient who starts adalimumab treatment after a washout period of 1 year without any previous treatment with adalimumab products.
Baseline and follow-up At cohort entry, the following data will be collected where available: age, sex, presence of obesity, presence of psoriatic arthritis, comorbidities (excluding psoriatic arthritis, including any of hypertension, ischaemic heart disease, stroke, pulmonary fibrosis, asthma, chronic obstructive pulmonary disease, diabetes, thyroid disease, peptic ulcers, hepatic disease, renal disease, demyelinating disease, epilepsy, depression, tuberculosis, cancer), psoriasis onset time, concomitant topical treatments (start and stop date), concomitant non-biological systemic treatments (start and stop date), previous biological treatments (excluding recent Humira treatment for switchers), ongoing adalimumab treatments (dose regiments, start and stop date), disease severity at baseline (Psoriasis Area and Severity Index-PASI measured at the closest date to the cohort entry date, within 1 year before).
During follow-up records, the following data will be collected where available: concomitant non-biological systemic treatments (start and stop date), adalimumab treatments (dose regiments, start and stop date, reason for discontinuation), AEs (type of AEs and date recorded), SAEs (type of SAEs and date recorded). The study end date is 31 December 2022. The maximum follow-up time will be determined according to the availability of data in each data source. We expect data for at least 1 year follow-up will be available in all involved databases. Registry databases follow-up participants during their healthcare visits or at least once a year. The SNDS follow-ups correspondingly to every reimbursed contact with health services.
Data analysis Establishing analytical cohort This study will implement the prevalent new-user cohort design with the following steps in delineating the analysis cohort. Separate pairs of biosimilar and matched originator cohorts will be identified for each biosimilar (ie, Amgevita vs Humira, Hyrimoz vs Humira, Imraldi vs Humira, etc). 1. Primary cohort formation: all eligible participants, including biosimilar and originator users, will be identified and included in the primary cohort. 2. Time 0 (T0) and exposure set identification: the first prescription of adalimumab for each user will be considered as T0 (day 0), and an exposure set will be formed for each month (each 30-day period) of adali-Open access mumab treatment since T0. All biosimilar users who received their first biosimilar prescription at time T (T months since T0) and all originator users who continued to use the originator at time T will be included in the exposure set at time T.
Where applicable (ie, for registry databases), reasons for discontinuation of each adalimumab biosimilar and originator will be analysed and classified as due to lack of effectiveness, due to AEs. Separate Cox proportional hazard models will be used to analyse overall discontinuation, discontinuation due to lack of effectiveness and discontinuation due to AEs. SAEs and AEs will be assessed by calculating incidence rate per person years. The risk of SAEs and AEs will be compared by calculating the ratio (IRR) between the two comparator groups. The HRs for the first incidence of SAEs will be estimated using the Cox proportional hazard models, with censorship occurring at the last available follow-up date or the end of follow-up. Multiple imputation models of 20 imputed datasets using chained equations will be performed to account for missing baseline data.
Multivariable regression model development The final set of covariates to be included in the models will be determined based on data availability in each data source. An a priori list of covariates was predetermined. These are covariates that were associated with biological drug survival in psoriasis in previous studies and are expected to be available in all data sources, including age, sex, presence of obesity, psoriatic arthritis, number of comorbidities (excluding psoriatic arthritis), disease duration, length of adalimumab treatment, number of previous biological treatments (excluding recent Humira treatment for switchers) and concomitant non-biological systemic treatments (at baseline and during follow-up). In addition, calendar year of treatment initiation will be included in the models for adjustment.
Meta-analysis A meta-analysis of study results will be produced using a random-effects model. Each dataset will provide independent HRs and 95% CIs for the meta-analysis. 22  Estimated overall drug survival rates will be calculated using the pooled HRs and their corresponding±95% CI will be used to calculate best-case and worst-case drug survival rates. For the metaanalysis of IRR, the number of events and the person-years of exposure in each database will be pooled.
DISCUSSIONS This study is anticipated to be the largest real-world cohort study investigating the effectiveness and safety of adalimumab biosimilars for the treatment of psoriasis. Using both routinely collected healthcare data and data from dedicated pharmacovigilance registries within the PsoNet initiative, this study draws on a diverse patient population from multiple countries, ensuring robust generalisability of study findings. To minimise selection bias and maximise the accuracy of study results, this investigation employs a prevalent new-user cohort design, which captures both switchers from adalimumab originator to biosimilars and patients who initiated treatment directly with adalimumab biosimilars. By accounting for previous adalimumab exposure, the study design controls for potential selection bias, confounders and enhances the validity of the observed outcomes.
Overall, the findings of this study will provide valuable insight into the real-world effectiveness and safety of adalimumab biosimilars for psoriasis treatment, enabling informed clinical decisionmaking for patients and healthcare providers. This study is subject to several limitations that may affect the validity of the results. First, the risk of bias due to missing data is a concern. The extent of bias depends on the type of missing data. 23  In the proposed study, we assume that baseline data are missing at random, that is, systematic differences between the missing values and the observed values can be explained by differences in observed data. Second, the accuracy of the data is dependent on the quality of the information documented in each database. Potential bias may arise due to misclassification of biosimilars and originator adalimumab.
Funding The  British Association of Dermatologists Biologic Interventions Register  (BADBIR) is coordinated by the  University of Manchester. BADBIR is funded by the  British Association of Dermatologists (BAD). The BAD receives income from  AbbVie ,  Almirall ,  Amgen ,  Celgene ,  Janssen ,  LEO Pharma ,  Lilly ,  Novartis ,  Samsung Bioepis ,  Sandoz Hexal AG  and  UCB Pharma  for providing pharmacovigilance services. This income finances a separate contract between the BAD and the  University of Manchester  who coordinate BADBIR. All decisions concerning analysis, interpretation, and publication are made independently of any industrial contribution. In the UK, the research is funded by the  Psoriasis Association  and supported by the  NIHR Manchester Biomedical Research Centre  ( NIHR203308 ).
| Study design The COMPOSER trial (NCT03157635) was conducted in compliance with the principles of the Declaration of Helsinki and in accordance with a written protocol approved by the institutional review board and/or independent ethics committees of each participating centre. Patients were enrolled at 14 sites in six countries (Germany, Japan, France, Hungary, Korea and Italy). COMPOSER's trial design has been previously described (Figure  S1 ). 20 The open-label COM-POSER trial enrolled healthy volunteers and patients with PNH.
macodynamics (PD) and efficacy of crovalimab have been investigated in the sequential, adaptive, four-part Phase I/II COMPOSER trial (NCT03157635). Part 1 of COMPOSER enrolled healthy volunteers. Parts 2, 3 and 4 of COMPOSER enrolled patients with PNH naive to treatment and switched from eculizumab for open-label treatment during a primary treatment period of 20 weeks, at the end of which patients were able to enter an open-label extension (OLE) period (Figure S1).
free C5 >0. 001 g/L or LIA values above the lower limit of quantification. Limitations of this Phase I/II, first-in-human study include the open-label single-arm design, the small sample size and the descriptive nature of the analyses. Additionally, LDH levels were assessed in local laboratories and the need for transfusion was based on investigator judgement only, potentially allowing for variability in the results. Overall, the promising efficacy and safety data reported here support the further clinical development of crovalimab. The optimised weight-based dose regimen of crovalimab (680 or 1020 mg SC Q4W) evaluated here is being further validated in a series of Phase III clinical trials. Ongoing Phase III trials include the global, randomised COM-MODORE 1 (NCT04432584) study evaluating crovalimab versus eculizumab in patients with PNH currently treated with C5 inhibitors; the global, randomised COMMODORE 2 (NCT04434092) study evaluating crovalimab versus eculizumab in people with PNH who have not previously received C5 inhibitor treatment; and the multicentre, single-arm COMMODORE 3 (NCT04654468) study studying crovalimab in C5 inhibitor-naive patients in China.
ACKNOWLEDGEMENTS The authors would like to thank the patients who participated in the trial, the patients' families and the investigators and staff at all clinical study sites. This study was funded by  F. Hoffmann-La Roche, Ltd  and  Chugai Pharmaceutical. Medical writing assistance for this manuscript was provided by  Akshaya Srinivasan, PhD , of  MediTech Media Ltd , and funded by  F. Hoffmann-La Roche, Ltd. We acknowledge support by the  Open Access Publication Fund of the University of Duisburg-Essen. Alexander Röth  provided consultancy for  Alexion ,  Amgen, Apellis, BioCryst, Bioverativ, F. Hoffmann-La Roche ,  Kira ,  Novartis  and  Sanofi ; received research funding from  F. Hoffmann-La Roche ; received honoraria from  Alexion, Bioverativ, F. Hoffmann-La Roche  and Novartis; and sits on advisory committees for  Alexion ,  Apellis, BioCryst, F.
SLC19A1 The first cGAMP conduit to be reported was reduced folate carrier 1 (SLC19A1/RFC1). SLC19A1 was initially characterized as an anti-transporter and main importer of reduced folate into cells. This couples the import of reduced/anti folates to the export of inorganic phosphates (29). Folates are essential membrane impermeable B family vitamins required for cell proliferation, tissue repair and development (29). Hence knockout of SLC19A1 in mice is embryonically lethal without continuous folate supplementation  (30). Initially, SLC19A1's role in cGAMP transport was characterized in the U937 and THP-1 human monocytic cell lines. SLC19A1 Cell types in which cGAMP conduits function and the CDNs they transport. Data collected for cells types in which conduit deficiency or specific chemical inhibition prevented STING pathway activation in response to exogenous CDNs.