dataesr/scientific-paragraphs-categorization huggingface dataset:
Some examples are the interRAI scales, validated with gold standard scales, and the CAPs (Clinical Assessment Protocols or Collaborative Action Points), which are trigger algorithms to alert caregivers of specific problem situations and provide guidelines for care planning. [8, 9]. The scales and trigger algorithms are automatically generated in the electronic application when the interRAI assessment is filled out. An OECD report from 2013 showed that the interRAI instruments enabled integrated eCare and allowed high quality data sharing. Moreover, quality indicators are available, which use aggregated data to identify the prevalence or incidence of outcomes within organisations, enabling benchmarking. To calibrate these quality measures, the indicators are risk-adjusted. These quality indicators were seen as very useful for quality improvement, as well as for public reporting and for benchmarking nationally and internationally  [3].
The interventions were considered to be innovative in the Belgian home care context because they were not part of regular care and they aimed at keeping frail older people longer at home. The interRAI HC was the main evaluation instrument in the Protocol 3 project and showed an added-value in the research and for policy making, especially as it made it possible to collect standardised and complete data about the people receiving care. Moreover, interRAI HC data was also collected in Belgium for the European project IBenC, involving 6 European countries  [31]. In the period between 2012 and 2015, the interRAI Palliative Care (interRAI PC) instrument was tested in 15 Belgian nursing homes  [32]. Results showed that de Almeida Mello et al. International Journal of Integrated Care DOI: 10. 5334/ijic.
In 2018, after more than 12 years of research, the Federal government has mandated the national implementation of the interRAI instruments in Belgium. Two IT-platforms were developed in order to complete the instruments: the BelRAI 2. 0 (Federal platform) and the Flemish BelRAI Platform (Flemish application). These platforms are linked and all data is securely saved in a central Federal database. The applications enable the transfer of client data between health care settings and therefore ensure continuity of care and facilitate multidisciplinary collaboration. Another feature of the applications is that professional caregivers can observe the evolution of their clients' outcomes (CAPs, scales and RUG groups) individually or in groups by using statistical software imbedded in the tool  [9].
INTERRAI IMPLEMENTATION IN SWITZERLAND The Swiss implementation followed a two-track path of upgrading the MDS-version and fully unlocking the potential of the Suite. When leveraged optimally, the interRAI mindset can become fully embedded in clinical routine processes to fuel decision making and to enable data transfer across the care continuum. The question however is how to build the road to reach that goal? The MDS 1. 0 instruments have been around for about two decades. For resource allocation in residential care, a Swiss adaptation of the MDS for nursing homes (MDS-NH) calculates a RUG-inspired case mix which is formalised in the federal law as one of three funding mechanisms. Its clinical application, the main driver of interRAI, however is underutilised in Swiss nursing homes. Home care agencies perform assessments in combination with registering clinical acts performed, mainly to inform and/or backup insurance claims (e.
Also, interRAI Cognitive Performance Scale (CPS) and interRAI Activities of Daily Living Hierarchy (ADLH) scales were automatically included from the electronic hospital record into the existing Vaudois transfer forms between providers. In 2021, home care data was linked to hospitalisation databases and population registries to explore the potential for illustrating care trajectories to inform policy  [42]. Next, to raise awareness pertaining to data-driven quality issues and to illustrate the potential of structured clinical routine data, Switzerland participated in an international benchmark that was shared with clinical and political decision makers  [43]. In the meantime, the national agencies prepared the nationwide upgrade of the interRAI LTCF, interRAI HC and interRAI CMH (2019-2022) as mentioned above.
This switch was beyond changing the item coding and involved software licensing to 15 software vendors, which included training adaptations and manual translations. In addition, efforts to sensitise users about the importance of obtaining generalisable data was crucial to guarantee the quality of the secondary use. The Swiss landscape is complex with multiple stakeholders, cantonal policies, three languages and varying rhythms. Both top-down and bottom-up movements drive progress. Centralised governance is crucial for dimensions like approval of reimbursement systems, integration into data warehouses, interoperability of software, definition of national quality indicators, etc. The small-scale pilots have generated greater appetite. Organisations expressed interest in broadening the interRAI portfolio to other populations such as pediatrics, addiction, palliative care, physical disabilities, and informal caregiver needs.
The pilot evaluated the training and education programs, implementation processes, and assessments of data from the three pilot sites. Following the conclusion of the pilot, national implementation of interRAI within Services for Older People commenced. An implementation framework was established, which details the requirements from both a top down and bottom up approach. National implementation of interRAI Ireland is progressing and is expected to increase in pace in 2022 following the re-procurement of an interRAI IT software vendor. The pilot, and subsequent roll out of interRAI identified a number of challenges and issues around interRAI implementation: -The impact of Information and Communication Technology (ICT) -both hardware and software issues -National policy and ideally supporting legislation is required.
INTERRAI IMPLEMENTATION IN ICELAND In the early 90's, a working committee for interRAI tools in Iceland was established within the Ministry of Health of Iceland. The first steps centered around various pilot projects, but also contained participation in crossnational studies. The version used was MDS 2. 0. The initial work led to its implementation nationwide in 1997. The focus was on the financial side, by implementing the 44 group RUG case-mix system. A few years later, quality indicators were developed. Data was being collected during admission to a nursing home, and then subsequently three times per year. Two of these assessments were full assessments and one was a short version, which only served the evaluation of the RUG category. The data was also used for research. Currently, the transformation to the more recent LTCF system is being worked on.
The vision is to implement and connect the complete suite of interRAI instruments and let the information flow from one service provider to the next as the client progresses through various parts of the elderly care system in Iceland. Special attention will be given on how the systematic assessment can transform and improve care of old people in Iceland by informing users and influencing their care pathways. Much more is needed to strengthen implementation and to foster the use of health information. This information is gathered for clinical use and is also used to help both service providers and policy makers in enabling them to make better decisions based on the extensive data the interRAI suite delivers.
ltd: to build up the very first prototype of a software solution to collect data and enhance feasibility of the use of the scales and other output; -STAKES: to create a benchmarking system in order to compare the case-mix, residents' needs, and quality of care between the various units; and -The participating units and or organisations in long term care: to see that every resident was assessed, and the nurses educated to use RAI. [31, 45, 46]. In connection to these studies, the interRAI-LTCF and interRAI HC instruments were translated into the Finnish language. Based upon discussions with stakeholders, in 2019 the Ministry of Social Affairs and Health was ready to propose to the Finnish Parliament to mandate interRAIassessment tools. The legislation then changed, and in July 2020, the government launched the Act on Supporting the Functional Capacity of the Older Population and on Social and Health Care Services for Older Persons (980/2012).
The goals were to adopt the interRAI instruments in the context of case management and care provision of services for older people in home care and residential care. According to this care act, the deadline for adopting interRAI instruments will be by April 2023. Currently the process of mandating is ongoing. THL has been assigned, by legislation, to collect the interRAI data nationally, and supervise the availability of instructions, and software (commercially available for organisations). Today, public information as to adopting and using the interRAI instruments, is publicly available at the website (https://thl. fi/en/web/ageing/assessmentof-service-needs-with-the-rai-system).
INTERRAI IMPLEMENTATION IN AOTEAROA, NEW ZEALAND The interRAI instruments are the agreed assessment tools used in Home and Community Support Services (HCSS) for people in Aotearoa New Zealand to support them to stay well in the community and to take increased responsibility for their own health and wellbeing. It is used to determine eligibility for publicly funded HCSS and to enter Aged Residential Care (ARC) where required. A regular assessment is also mandated in ARC to inform the resident's care plan. Information collected during the assessment is stored in a live data warehouse enabling population data to be collected without any extra effort from the person or their assessor. The aggregated data is available for planning, policy and research purposes to improve population health outcomes.
In addition, the standardised functional data help clinicians to objectivise clinical changes over time. Another shift mentioned was the broadening of the assessors' job from strictly clinical disciplines to non-nursing disciplines, which seems to be greatly appreciated in practice. Another added-value of the interRAI Suite is that it works as a decision support for care planning, based on valid and standardised data. In France, in particular, the implementation of the interRAI assessment brought more legitimation to the role of the case manager, who found the link between the comprehensive assessment and the care planning very useful. A recent study identified the most relevant barriers and facilitators for the use of a clinical decision support system (CDSS) reported by 581 physicians from 11 European countries.
Technical issues, having to indicate a reason for overriding an alert and unclear advice were perceived as the most important barriers, as well as time-expenditure of the CDSS, the need of integration with existing systems and accurate advice for medically complex clients. Relevant facilitators were a CDSS that is beneficial to patient care, easy-touse and easily accessible, contributed to increased work efficiency, fits into the physician's workflow and supportive to the decision-making process  [52]. Most countries reported technical aspects as barriers for implementation, such as difficulties to access electronic records and unavailability of IT infrastructure, as well as a low interoperability when the interRAI software is not compatible with other systems already in use. These compatibility issues make the assessment very time consuming, as the instruments may overlap with other data collected via other software used by the organisations.
LESSONS LEARNED Research clearly shows that the interRAI suite of instruments has a great potential to: -Enhance multi and interdisciplinarity, -Allow the transfer of client-centered data between and within different care settings, -Enhance integrated care, -Improve quality of care. In all seven countries, an IT platform was developed to support the assessment and its features, as well as to allow information sharing. However, the development and implementation of information technology is a gradual and long process, which also has to count on the trust and acceptability of caregivers to work with the new IT platform. Users must be convinced that the platform is safe and that the electronic assessments can contribute to quality of care and to benefits in the care practice.
de Almeida Mello et al. International Journal of Integrated Care DOI: 10. 5334/ijic. 6968 de Almeida Mello et al. International Journal of Integrated Care DOI: 10. 5334/ijic. 6968 NEW ZEALAND NEW ZEALAND Policy makers and researchers: Collection of information once in a live national database that can be used for many purposes, such as understanding the needs of the assessed population, making evidence based decisions, understanding of health outcomes (equity), allowing case mix funding and resource planning Clinicians and care organisations: • Common language across settings • Decision support properties for assessors to inform care • Transparent and objective review of assessment quality • Supports prioritisation and eligibility processes • One single platform (assessment record) to exchange information.
With the previous hypothesis, this deviation could be misinterpreted as a change in the land-surface characteristics. Padrón et al. (2017)  found that the variability in the parameter is highly correlated to climate features such as snow fraction precipita tion and the storm arrival rate. Over their extensive global database, the correlation between vegetation indices and direct anthropogenic influence factors is only secondary. Jaramillo et al. (  2022 ) used CMIP6 multi-models ensemble to fit Budyko curves over several basins for the period 1901-1950 and to calculate ET/P PET/P for 2051-2100. They compared the results of the ensemble to those obtained with the hypothesis that catchments should follow their initial Budyko curve. Most basins will not follow the curve under climate change, showing a climate dependence of the deviation from the initial curve.
A "Natural Reference" Simulation This study uses the LSM Organizing Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) from the Institut Pierre Simon Laplace. It includes biophysical and biogeochemical processes to simulate the global carbon cycle and quantify terrestrial water and energy balance. It runs coupled to an atmospheric model or in stand-alone conditions with an independent data set to force the atmospheric conditions. Here, we use the model in stand-alone conditions, forced with the data set GSWP3 covering 1901-2013  (Hyungjun, 2017)  at the resolution of 0. 5° for all climate variables. The hydrological network of the ORCHIDEE LSM is constructed from the hydrological elevation model Hydro-SHEDS  (Lehner et al. , 2008) , which covers the area studied with the resolution of 30 arc s (approximately 1 km at the equator).
The hydrological information is then upscaled to the resolution of the atmospheric grid, the hydrological coherence being preserved by the construction of hydrological transfer units at the subgrid level  (Polcher et al. , 2022). From a database of gauging stations, upstream basins are reconstituted on the hydrological elevation model grid and then projected on the atmospheric grid during the process. We have access to 2,134 stations over the area studied for which the LSM calculates a discharge and for which we have the reconstituted upstream basin (Figure  S1  in Supporting Information S1). The LSM ORCHIDEE, more specifically the Schématisation des EChanges Hydriques à l'Interface Biosphère-Atmosphère module, uses the USEB (unstressed surface energy balance) method to model PET (details in  Barella-Ortiz et al.
We study Q variations and not the absolute value of Q since we know that the output of the LSM does not represent the complete processes over real catchments. We focus here on the impact of the changes in atmospheric parameters on land surface responses with constant characteristics. The modeled watersheds react to the climate data input at each time step (30 min time step). Therefore, the LSM output depends on both the evolving annual average and the evolving distribution over the year of the climate variables. For consistency in the calculation of E and PET, we take both from the output of ORCHIDEE forced with GSWP3. The gridded outputs (PET, E) are at the resolution of the forcing data set (0. 5°). P is the sum of rainfall and snowfall in GSWP3. Then, we consider the annual mean P, PET, and E over hydrological years, integrated over each catchment.
Synthetic Forcings to Analyze the Effect of Variation of Seasonality In order to better understand the effect of inter and intra-annual climate variations on the Budyko framework and on discharge Q, we construct synthetic climate forcings, fixing one or the other. The calculation of PET includes many related climate variables and nonlinear relationships, making it very difficult to anticipate how a change in a given climate variable may influence its behavior. It is, therefore, too complicated to create synthetic forcings for which we can modify climate variables to fix PET seasonality, for instance. Therefore, we only modify the precipitation P in the synthetic forcings to see how it impacts our results compared to the reference forcing. The reference forcing is the GSWP3 data set from September 1901 to September 2012 (3 hr time step).
Data Availability Statement The forcing data set GSWP3 used to grid P and other climate data and run the LSM over Europe between 1901 and 2010 in the study is freely available upon registration  (Hyungjun, 2017). The LSM used to calculate PET and model the discharge in this study is ORCHIDEE (IPSL [Institut Pierre Simon Laplace], 2017), available on their website. The outputs used for this study at the annual time step for each catchment are gathered in a file freely available on Zenodo. org  (Collignan et al. , 2023). Stations used in the study come from the Global Runoff Data Centre (GRDC) (2022), completed with the Geoportal of Spain Ministerio (Ministerio para la Transición Ecológica y el Reto Demográfico, 2020) and over France with data from the database HYDRO (Ministere de l'ecologie, du developpement durable et de l'energie, 2021), where the data are freely accessible but have to be gathered region by region and station by station.
The file on Zenodo. org  (Collignan et al. , 2023 ) also includes the list of the stations used in the study and their main related metadata: their location and the size of the upstream area used to position the station on the grid. The upstream watersheds are reconstructed using the hydrological elevation model HydroSHEDS  (Lehner et al. , 2008)  to construct the routing graphs for rivers on the LSM grid.
JCI Insight 2022;7(18):e161783 https://doi. org/10. 1172/jci. insight. 161783 Having shown that FA metabolism was impaired in a population of injured PTCs unable to overcome cell stress initiated by IRI during progression to CKD, we wondered whether the Cpt2 -Acsl4 + Acsl5 + Acsm5 - gene signature was a feature of established CKD. We examined public repositories pertaining to the transcriptome of mRNA isolated from tubules of diabetic mice and whole-kidney tissue from a transgenic mouse model expressing mutant uromodulin (UmodC147W/+) (NCBI Gene Expression Omnibus [GEO] database accession no. GSE30122 and GSE102566, respectively) and found that expression of this set of FA-related genes had similar trends to that observed in the progression after IRI in the 2 CKD models: Cpt2 and Acsm5 were significantly downregulated in CKD, whereas Acsl4 and Acsl5 were upregulated (Figure  4, B  and C ).
The general pathogenic mechanism of lipotoxicity in the kidney has been proposed to be an overload of intracellular free FAs, associated with an accumulated triglyceride pool, with subsequent reactive oxygen species production and inflammasome activation  (53) , ceramide accumulation  (54) , mitochondrial dysfunction  (55) , DNA damage  (56) , cell cycle arrest  (57) , enhanced lysosomal activity and impaired autophagy  (55) , and ER stress  (58). All these perturbations occurring within PTCs drive tubulointerstitial inflammation, fibrosis with PTC apoptosis, and secretion of a combination of profibrogenic and proinflammatory factors  (25). However, the mechanisms that govern intracellular lipid accumulation in the diseased kidneys remain undefined. The consensus model is based on glomerular damage, leading to an from public repositories (GEO accession GSE102566).
The column temperature was 50°C, and the flow rate was 200 μL/min. The metabolites were detected across a mass range of 75 to 1,000 m/z using the Q Exactive Plus mass spectrometer (Thermo Fisher Scientific) at a resolution of 35,000 (at 200 m/z) with electrospray ionization and polarity switching mode. Lock masses were used to ensure mass accuracy below 5 ppm. The peak areas of different metabolites were determined using Thermo Fisher Scientific TraceFinder software using the exact mass of the singly charged ion and known retention time on the HPLC column. Our metabolomics analyses are focused on small polar compounds in central carbon metabolism. We applied an established and referenced method for sample extraction and LC-MS analyses using pHILIC HPLC column for polar metabolites' separation  (71).
Data analysis was performed in the MetaboAnalyst 5. 0 software  (72). Each urine metabolite was normalized to urine creatinine level to avoid dilution bias. Samples were normalized by sum and each metabolite level was adjusted by autoscaling. PCA was performed including all the metabolites identified in KTR urine at 3 and 12 months to determine the impact of the time on urine metabolome. Hierarchical clustering was performed using the Ward method including all the metabolites identified in the KTR urine.
Animal experiments Tun injection. At 12 weeks old, C57BL/6 background male mice (Charles River Laboratories) were intraperitoneally injected with Tun (MilliporeSigma, T7765) (1 mg/kg) or vehicle (DMSO) at day 0, and mice were sacrificed 2 days postinjection (n = 4-5 mice per condition). Total RNA was extracted from kidneys using the RNeasy Mini Kit (QIAGEN) according to the manufacturer's protocol. UmodC147W/+ mice. A detailed description of the methods for the UmodC147W/+ mouse line of the C57BL/6J genetic background was previously reported  (40). Public repositories pertaining to the transcriptome of mRNA isolated from whole-kidney tissue from mutant mice and littermate controls at multiple time points, including 12 and 24 weeks (GEO database accession GSE102566), were analyzed for Acsl4, Acsl5, Acsm5, and Cpt2 expression.
Bilateral IRI for bulk RNA-Seq. The surgical procedure has been detailed  (18). A 21-minute warm renal IRI was performed on 10-to 12-week-old (25-28 g) C57BL/6CN male mice. The procedure of bulk RNA-Seq of kidneys after bilateral IRI has been detailed  (18). GEO database accession GSE98622 data were analyzed for Acsl4, Acsl5, Acsm5, and Cpt2 expression at 4 hours; 1, 3, 7, 14, and 28 days; and 12 months after IRI (n = 3-4 mice per condition). Bilateral IRI for snRNA-Seq. snRNA-Seq data were obtained from experimental IRI models previously described  (18). Bilateral IRI was induced by clamping the renal pedicle with a nontraumatic microaneurysm clamp for 18. 5 minutes in 8-to 10-week-old male C57BL/6J mice. Mice were euthanized at 4 hours, 12 hours, 48 hours, 14 days, and 6 weeks after IRI  (73).
In the other data set, bilateral IRI was induced by clamping the renal pedicle with a nontraumatic microaneurysm clamp for 15 minutes in 10-to 12-weekold, 25-28 g, male C57BL/6J mice. Mice were euthanized at 64 hours, 96 hours, and 28 days after IRI  (74). The procedure of snRNA-Seq of kidneys after bilateral IRI has been detailed in  (73, 74). GEO database data (GSE151167 and GSE139107) were analyzed for Acsl4, Acsl5, Acsm5, and Cpt2 expression.