dataesr/scientific-paragraphs-categorization huggingface dataset:
The bundle was implemented and disseminated based on a multimodal approach, targeting physicians and nurses. The sample comprised patients hospitalized in the ICU aged ≥ 18 years and submitted to endotracheal intubation for more than 48 h. Adherence to the bundle was assessed through auditing by observation. Outcome data were collected daily and entered into an intern database. Results were processed by the Statistical Package for the Social Sciences. The study was approved by the Ethics Committee and the hospital's Board of Directors following the ethical principles for health research.
Results: Seventy one percent of staphylococcus aureus isolates in the national database were resistant to ampicillin compared to 78. 6% at the county (N = 22)0. 85% of Escherichia coli isolates in the national antibiogram were resistant to amikacin compared to 50% of Escheri- chia coli isolates (N = 16) at Kitale county hospital. 73% 0f Acineto-bacter baumanii species in the national antibiogram were resistant to Piperacillin/tazobactam compared to 100% at the referral hospital. Pseudomonas aeruginosa isolates exhibited the least resistance to antibiotics with 0% resistance to amikacin, ceftazidime, gentamicin, imipenem and meropenem in comparison to 8%, 33%, 15%, 31% and 20% for the same organism nationally.
Objectives: This study aims to provide a comprehensive estimate of P92 A Vancomycin-resistant Enterococci, Methicillin-resistant Staphylococ-cus aureus, multi-drug resistant Klebsiella and Acinetobacter were not significantly different. There was a decreasing trend of usage for AMR burden in Nepal. Piperacillin and Tazobactam (p < 0. 001), Levofloxacin (p = 0. 033) and Meropenem/Imipenem (p < 0. 001). Whilst Vancomycin (p = 0. 042) and Linezolid (p = 0. 01) saw sporadic increases in usage during the COVID period. Conclusion: COVID-19 had limited impact on antimicrobial resist- ance in this tertiary care hospital in Singapore, possibly due to hos- pital staff maintaining a high level of IPC practices despite the high Methods: This cross-sectional study utilized data from the Institute for Health Metrics and Evaluation's MICROBE (Measuring Infectious Causes and Resistance Outcomes for Burden Estimation) database.
This systematic review aimed to update what is known about the prevalence, risk factors, and aetiologic agents of endemic HCAI in Africa. Methods: We comprehensively searched MEDLINE/PubMed, CINAHL, and Global Health databases for studies published in English and 1,2 1 Malawi Liverpool Wellcome Programme, Kamuzu University of Health Sciences, Blantyre, Malawi, 2 Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, United Kingdom Correspondence: G. K. Bunduki Antimicrobial Resistance & Infection Control 2023, 12(Suppl 1): Antimicrobial Resistance & Infection Control 2023, 12(Suppl 1):P140 Introduction: The burden of healthcare-associated infections (HCAI) remains poorly described in Africa. Objectives:.
Objectives: We aim to investigate the role of sinks as a risk factor for infection and colonization with GNB in the ICU in non-outbreak settings. Methods: We performed a systematic review on the effectiveness of interventions on sinks in ICU in non-outbreak settings. The review has been registered at PROSPERO (CRD42022336366). We searched Medline via PubMed and Embase via Ovid and ClinicalTrials. gov without language or date restrictions. Studies of any design were included if they described an intervention on the water outlets in patient rooms and presented data about nosocomial infection or colonization rates. We excluded studies reporting outbreaks or focusing on Legionella species. tings. Heterogeneity in study design, and low quality of evidence make it difficult to evaluate single measures.
Hospitals are being increasingly encouraged to adopt ecological practices to reduce their impact on the environment. Many of the current environmental protection (EP) measures in hospitals cover fields that are relevant to infection prevention/control (IPC). However, it is unclear to what extent EP measures undertaken in hospitals have taken an IPC perspective into account. Objectives: This rapid review aims to give an overview of studies that consider the interaction of EP and IPC endpoints in hospitals. Methods: The databases Pubmed and Web of Science were searched on 14. 04. 2023 with no time limit. Abstracts were screened and papers were selected by 3 independent reviewers using predefined inclusion and exclusion criteria. Conclusion: Targeted interventions, including staff education and administrative follow-up, can improve compliance with anti HBs titre monitoring among HCWs.
2% for to January 2023. Data was collected among all hospitalized patients imipenem and meropenem respectively. Staphylococcus aureus was highly sensitive to linezolid, Moxifloxacin and Mupirocin (100%, 84, 6% and 76, 9% respectively). carrying MultiDrug-Resistant bacteria (MDRb) at Mahdia University Hospital based on notification by the bacteriology laboratory. Binary logistic regression model was used to assess associated factors. SPSS software was used for data entry and statistical analysis. Results: A total of 155 patients with MDR bacterial infections (MDRbi) were recruited, with a mean age of 49. 9 ± 21 years old and a sex ratio of 1. 7. Half of them were hospitalized in Intensive Care Units. Almost Antimicrobial Resistance & Infection Control 2023, 12(Suppl 1)M. G. M. El Sherbeny 1,* , M.
Methods CFD analyses were performed at the University of Sheffield, UK on retrospective clinical data collected from adult patients (≥18 years old) with suspected myocardial ischaemia with non-obstructive coronary arteries (INOCA) undergoing coronary angiography and Rayflow catheterization at the Catharina Hospital, Eindhoven, NL. Patients provided informed consent and the study was approved by the research ethics board [Medical research Ethics Committees United (MEC-U)]. All supporting data are provided in this manuscript and in the supplementary material. A derivation analysis of Murray's exponent, based upon the same raw clinical dataset as this study, has already been published. 15.
Clinical data collection FFR was assessed in arteries of interest using standard technique (PressureWire™ X, Abbott, MN, USA), with a maximum tolerated signal drift of 2 mmHg,  16  and the Rayflow™ infusion catheter (Hexacath, Paris, Fr) and the Coroventis™ (Abbott, Plymouth, MN) system,  11, 12  were used to quantify Q (Q CIT ) and Rmicro (Rmicro CIT ). Thermodilution measurements were taken in the proximal coronary artery, with pressure readings under saline-induced hyperaemia taken at the location of 6 cm distal to the tip of the infusion catheter. 12 Pseudonymized angiography (DICOM) and physiological (pressure and flow) data were exported to the University of Sheffield for computational processing in February 2021. Full exclusion criteria have previously been published. 15 In brief, cases were excluded if transferred clinical data were unsuitable for computational processing or major arterial side branches were present within 3.
Statistical analysis Categorical variables are presented as frequency (percentage). Normally distributed continuous variables are presented as mean (standard deviation), while those with a non-normal distribution are presented as median [interquartile range]. Normality of data distribution was assessed using the Shapiro-Wilk test. For parametric data, mean values of haemodynamic parameters were compared using an unpaired t-test, while a Mann-Whitney U test was used for non-parametric data. Correlation was quantified using Pearson's correlation coefficient (r) and agreement was assessed with both Passing and Bablok regression and Bland Altman plots. Log-transformation was used for skewed datasets. Analyses were performed using MATLAB R2021a (Math Works Inc. , USA).
Clinical cases From the original 48 cases, 27 cases from 20 patients yielded full physiological datasets. Cases were excluded due to insufficient angiographic quality (n = 2) and pressure (n = 1) data, insufficient angiographic projections (n = 11), unsuitable patient anatomy (n = 3), coronary intervention performed prior to Q CIT measurement (n = 2), and major side branches present ≤3. 0 cm of the Rayflow catheter (n = 2). Seven patients (35%) were male, the mean age was 62 (±10) years, and the mean body mass index (BMI) was 25. 1 ). No included cases contained diffuse epicardial disease. Using a threshold of 460 mmHg min/ L,  [25] [26] [27]  five (25%) patients had clinically significant MVD as assessed by Rmicro CIT.
9 ± 35 mL/min (95% limits of agreement -70 to +68 mL/min; mean Q across all included vessels 176 mL/min). 29 n the present study, we reported moderate correlation between CIT and CFD Q for the regional porous wall boundary method. The 95% limits of agreement were ±175 mL/min. These limits of agreement are larger than reported in previous studies of different datasets,  12, 13, 28, 29  even when accounting for the larger mean Q of patients included in the current study. Several factors may have negatively influenced agreement, the most important of which is likely to be characteristics of included arteries. Q CFD accuracy is critically dependent upon agreement between simulated flow patterns and those occurring invivo. In healthy arteries, coronary flow is governed predominantly by Poiseuille (viscous) effects and as such, results are extremely sensitive to errors in reconstruction diameter.
Patient Population and Data Collection Data were obtained from two prospective French cohorts: (i) the AMINO2 study  [28] , including patients from the Nîmes University Hospital between October 2014 and February 2015, and (ii) the PICAMI study  [6] , including patients from the Nantes University Hospital between July 2014 and November 2016. Both studies were approved by local review boards. We included patients over 18 years old and treated for sepsis in the ICU with combination therapy including aminoglycosides at the day of enrolment. According to French guidelines, patients received a 30-min intravenous infusion of amikacin diluted in 50 mL of NaCl 0. 9%. Plasma concentration sampling was performed 30 (±15) minutes after the end of infusion (C 1h ). For all patients at the day of inclusion, the following kinds of data were collected: age, sex, height, and SAPS II  [42].
On every day of amikacin infusion, the following data were additionally collected: weight, serum creatinine, total proteinemia, albuminemia, renal replacement therapy, vasopressors use, and invasive mechanical ventilation. Body mass index and body surface area (BSA) were calculated with the Dubois formula  [43] , ideal body weight (IBW) was calculated with the Lorentz formula  [44] , ABW was calculated with the Traynor formula  [45] , estimated GFR was indexed by BSA according to the CKD-EPI formula  [30]  (eGFR, mL/min/1. 73 m 2 ), and estimated GFR was calculated with the Cockroft and Gault and the Modification of Diet in Renal Disease (MDRD) formulas.
PTA and Dosing Nomogram Monte Carlo simulations (n = 1000), based on the parameters of the structural model, were generated from patient profiles with a large panel of covariate values (eGFR and TBW) between the 10th and 90th percentiles of the population. For each of these profiles, exposure to amikacin was assessed for doses ranging from 1300 mg to 5800 mg administered in a 30-min intravenous infusion. Targeted amikacin concentrations were defined as C 1h > 8 × MIC when MIC = 8 mg/L (64 mg/L) according to French guidelines  [11]  and EUCAST breakpoints  [12]. Optimal doses to achieve this PK-PD target were determined for various eGFR values and for TBW values equal to the 10th, median, and 90th percentiles of the observed weight in the dataset. Exploratory simulations were conducted to determine the PTA of C through target ≤ 2.
Acknowledgments: We would like to acknowledge the PICAMI and AMINO2 study groups:  S. Corvec ,  J. Caillon ,  D. Boutoille ,  B. Nucci ,  B. Louart ,  A. Friggeri ,  H. Knani ,  A. Evrard ,  J-P. Lavigne ,  B. Allaouchiche ,  J-Y. Lefrant ,  J. Roberts , and  L. Muller.
This method locates the thermocline from the base of the mixed layer to the depth at which temperature has dropped halfway toward the deep-water temperature at 400 m  (Fiedler, 2010). Despite few studies applying the above methods to particular regions of the ocean, to the best of our knowledge, there are no studies addressing the MTD on a global scale. The methodologies mentioned above to locate the MLD use the data from the profilers of the Argo program. Argo is an international program that measures the ocean water column using a fleet of autonomous profilers, which are capable of measuring pressure, temperature, salinity, and biogeochemical parameters (Argo, 2022a). These profilers move along ocean currents and measure the water column by making profiles from a depth of two kilometers to the surface.
Data collection For all the diagnostics carried out in this study we used the Argo dataset. We downloaded the snapshot of January 2022 (Argo, 2022b) and we used the profiles already evaluated by the delayed mode quality control (DMQC) from January 1998 to December 2021 (more than two million). We selected pressure, temperature and salinity profiles from the core Argo floats, which typically sample down to 2000 m. We then transformed the in-situ temperature and practical salinity into conservative temperature (Θ), and absolute salinity (S A ), using the definition of the Thermodynamic Equation of SeaWater 2010 (TEOS-10)  (McDougall and Barker, 2011).
The MLD is then determined as the depth where the potential temperature is 0. 2°C higher (or lower) than the reference temperature at 10 m (de Boyer  Montégut et al. , 2004). To locate the MTD, we used the same procedure but going upwards in the function, in this case we take the reference temperature where the deep layer should be located and we look for the difference of 0. 2°C by decreasing the depth through the function. Because the method is based on a single nonlinear function adjustment, we can have a precision of even centimeters. The procedure explained above can be seen in Figure  1a  and can be used through the script developed  (Romero et al. , 2022). This methodology was applied to each of the DMQC Argo profiles. Before we apply our method to locate the MTD, we have validated it by comparing our results for the MLD with other existing methods.
Code availability. The methodology presented in this study was developed in Python 3. 7 and is licensed under a Creative Commons Attribution 4. 0 International License. The source code is available at https://doi. org/10. 5281/zenodo. 6985561  (Romero et al. , 2022). The latest package version is v1. 0. 1. Data availability. These data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo. ucsd. edu, last access: January 2022; https://www. ocean-ops. org, last access: January 2022). The Argo Program is part of the Global Ocean Observing System. The data used belongs to the snapshot of January 2022 (Argo, 2022b).
Instrument The online survey collected data on stroke, such as general knowledge, risk factors, early symptoms, and consequences, as well as demographic and socioeconomic factors. Pilot testing was performed on 35 individuals, and the data collected were not included in the final analysis. The survey was self-administered by participants and was available in Arabic, the native language of Saudi Arabia. A written Participant Information Statement (PIS) describing the main purpose of the study and the expected time required to complete the questionnaire (10-15 min) was provided to participants at the start of the survey. The survey was developed based on previous literature that used a set of questions to accurately capture multiple aspects of stroke awareness and general knowledge in different populations  (13) (14) (15) (16) (17).
Residual confounding bias is also possible since there might be factors related to stroke awareness that were not measured in this study. Furthermore, we did not validate the knowledge scale, and we assessed only the level of awareness regarding stroke. In addition, our analysis regarding the identification of one symptom/risk factor for stroke was based on other studies  (13) (14) (15) (16) (17). On the other hand, our study has several strengths as it added data to the literature about stroke disease awareness which highlighted the lack of knowledge regarding stroke among the Saudi population.
Despite host-fungal symbiotic interactions being ubiquitous in all ecosystems, understanding how symbiosis has shaped the ecology and evolution of fungal spores that are involved in dispersal and colonization of their hosts has been ignored in life-history studies. We assembled a spore morphology database covering over 26,000 species of free-living to symbiotic fungi of plants, insects and humans and found more than eight orders of variation in spore size. Evolutionary transitions in symbiotic status correlated with shifts in spore size, but the strength of this effect varied widely among phyla. Symbiotic status explained more variation than climatic variables in the current distribution of spore sizes of plant-associated fungi at a global scale while the dispersal potential of their spores is more restricted compared to free-living fungi.
Assembly of spore database Unlike macroorganisms such as plants and animals, no databases of offspring morphology for fungi exist. Therefore, to answer our questions, we created and populated a new database by text mining nearly 100,000 taxonomic descriptions deposited in Mycobank  (Robert et al. , 2013)  (http://www. mycob ank. org/; see Supplementary material for further details). In total, we collected information on spore width and length dimensions for >26,000 accepted species (based on taxonomy from the Catalogue of Life; https://www. catal ogueo flife. org/), representing 20% of all described fungal species (Figure  S1 ). This database includes spore-dimension data from both sexually and asexually produced spores across major fungal lineages at different stages of fungal life cycles.
However, we restricted the analysis described below to sexual spore types described as 'ascospores' and 'basidiospores' (henceforth referred to as 'sexual spores') and asexual spore types described as 'conidia' and 'sporangiospores' (henceforth referred to as 'asexual spores') because they represent the most frequently occurring types of spores in our dataset and thus can be compared across several fungal lineages and symbiotic groups (Figure  1c ; Figure  S2 ). We also excluded spores of glomeromycete fungi for our main analyses because their extreme large size may bias the results (see Figure  S2  and supplementary material for specific spore definitions and nomenclature used in the analysis;  Kendrick, 2017). We then calculated spore volume using width and length as a proxy for spore size following the formula for a prolate spheroid  (Aguilar-Trigueros et al.
Assembly of symbiotic status data We also assembled a symbiotic status database (by mining and crosschecking different functional databases) where fungal species were categorized as asymbiotic (i. e. saprotrophic species that have only been reported as free-living during their whole life cycle) or symbiotic with a wide diversity of host and types of interactions as follows: (1) Asymbiotic saprotrophs; (2) Insect pathogens; (3) Lichen fungi; (4) Plant endophytes; (5) Plant pathogenic necrotrophs; (6) Plant pathogenic biotrophs; (7) Ectomycorrhizal fungi; (8) Arbuscular Mycorrhizal fungi; (10) Human pathogens. For plant symbiotic fungi, we further classify the level of specialization as either facultative symbiosis (species that are reported to alternate between a free-living and symbiotic phase) or obligate symbiosis (species that have been exclusively reported as symbiotic to complete their life cycle) based on the biology of their respective symbiotic group (see supplementary material for data sources and details on the criteria used to define symbiotic groups).
e. it is not specific to only a subset of fungal clades). However, because this tree only includes a limited number of species, we also used a taxonomy-based phylogeny consisting of 23,000 species from which we obtained taxonomic data from phylum-to-species level using the function as. phylo from ape package  (Paradis & Schliep, 2018 ) (see further details in the supplementary material for the construction of this tree). Independently for each phylum or fungal group (i. e. Ascomycota, Basidiomycota, zygomycetous fungi, zoosporic fungi and microsporidan fungi), we conducted phylogenetic linear regression models where the logarithm of spore volume was the response variable, symbiotic status (based on the 10 symbiotic guilds classified here) was the explanatory variable and either the genome-based phylogenetic tree from  (Li et al.
, 2021)  or the taxonomy derived cladogram was used to account for phylogenetic relatedness. These phylogenetic regressions were conducted on sexual spores of the Ascomycota and Basidiomycota (i. e. ascospores and basidiospores) and asexual spores of all phyla and fungal groups (as defined above, we only include asexual spores referred as 'conidia' or 'sporangiospores') separately as they represent two separate traits under different selection. These phylogenetic linear regression models were conducted using the function phylolm from the phylolm package  (Ho et al. , 2016). We conducted additional phylogenetic regression models testing whether spore size is bigger for obligate symbionts compared to facultative symbionts for sexual spores of plant-associated fungi in the Ascomycota and Basidiomycota and asexual spores of the Ascomycota.
Relative importance of symbiotic status against climate variables in explaining spore size variation across communities To obtain climatic information, we first mapped the geographic distributions of fungal species observed in several large-scale, high-throughput DNA-sequencing studies of fungal communities from soil and plant samples covering an extensive breadth of biomes and occurring on all seven continents (Figure  S5 ; see supplementary material section for details on how species annotations were performed). Then, we collected climatic data associated with the locations where those species were found, estimated mean values for each species, and compared the ability of those climate variables and each species' symbiotic status to explain variation in spore size. For this analysis, we focused on species that in our database are reported to produce only one spore type because it is not possible to determine the spore type associated with environmental DNA sequences.
Differences between saprotrophic and plantassociated fungi in the relationship between spore size and geographic spread We assessed the role that fungal lifestyle plays in determining the relationship between geographic range and spore size. As with the previous analysis, we focused on species that in our database are reported to produce only one spore type because it is not possible to determine the spore type associated with environmental DNA sequences. To do this, we estimated species' geographic ranges from their mapped distributions in environmental DNA-sequencing studies. Specifically, geographic range for each species was estimated in two ways: (1) as the maximum distance in meters between samples, in which the species was detected using the ellipsoid method  (Vincenty, 1975 ) calculated with the distVincen-tyEllipsoid function from the 'geosphere' package in R  (Hijmans et al.