bluuebunny/crossref_metadata_2025_split huggingface dataset:
How patient experience survey results might guide hospital quality improvement strategy: a thematic and syntactic analysis of patient comments.
Authors: Crubezy Marion , Douay Céline , Michel Philippe , Haesebaert Julie 
Published: February 2023

Abstract
Background.
Although patient experience surveys flourish in many countries with the aim to improve quality of care, questions remain concerning their ability to become effective drivers of change within institutions. The patient comments from the French national patient experience hospital survey were analysed using an innovative structured approach to characterise patient experience and identify field actions for the institutions.
Methods.
The comments were taken from the two open-ended questions comprised in the patient experience survey of the Hospices Civils de Lyon between 2018 and 2019. The comments analysis methodology consisted in three steps: thematic analysis; syntactic analysis; generation of statistics for the creation of a patient journey and prioritisation of sub-themes. The STROBE statement checklist was followed.
Results.
Over a year, 79.7% of the 7 362 respondents left at least one comment at the end of the survey and were included in the study, for a total of 5 868 surveys and 10 061 comments. These led to the identification of 28 general themes and 184 specific sub-themes. From the patient journey created, 23 sub-themes were prioritised and gathered into four key categories: relationship between patient and staff; environment; surgery and pain management; information and care coordination. For each of them, the actions and expectations formulated by the respondents were described.
Conclusions.
The analysis of patient comments obtained from a standardised survey allowed to characterise the patient journey using data that describes patient experience, enabling a prioritisation of actions aiming to improve practice and quality of care at the institution, department, and staff level.
Effectiveness of System Navigation Programs Linking Primary Care with Community-Based Health and Social Services: A Systematic Review
Authors: Teggart Kylie , Neil-Sztramko Sarah E. , Nadarajah Abbira , Wang Amy , Moore Caroline , Carter Nancy , Adams Janet , Jain Kamal , Petrie Penelope , Alshaikhahmed Aref , Yugendranag Shreya , Ganann Rebecca 
Published: February 2023

Abstract
Background
Fragmented delivery of health and social services can impact access to high-quality, person-centred care. The goal of system navigation is to reduce barriers to healthcare access and improve the quality of care. However, the effectiveness of system navigation remains largely unknown. This systematic review aims to identify the effectiveness of system navigation programs linking primary care with community-based health and social services to improve patient, caregiver, and health system outcomes.
Methods
Building on a previous scoping review, PsychInfo, EMBASE, CINAHL, MEDLINE, and Cochrane Clinical Trials Registry were searched for intervention studies published between January 2013 and August 2020. Eligible studies included system navigation or social prescription programs for adults, based in primary care settings. Two independent reviewers completed study selection, critical appraisal, and data extraction.
Results
Twenty-one studies were included; studies had generally low to moderate risk of bias. System navigation models were lay person-led (n = 10), health professional-led (n = 4), team-based (n = 6), or self-navigation with lay support as needed (n = 1). Evidence from three studies (low risk of bias) suggests that team-based system navigation may result in slightly more appropriate health service utilization compared to baseline or usual care. Evidence from four studies (moderate risk of bias) suggests that either lay person-led or health professional-led system navigation models may improve patient experiences with quality of care compared to usual care. It is unclear whether system navigation models may improve patient-related outcomes (e.g., health-related quality of life, health behaviours). The evidence is very uncertain about the effect of system navigation programs on caregiver, cost-related, or social care outcomes.
Conclusions
There is variation in findings across system navigation models linking primary care with community-based health and social services. Team-based system navigation may result in slight improvements in health service utilization. Further research is needed to determine the effects on caregiver and cost-related outcomes.
A predictive model for the unplanned interruption during continuous renal replacement therapy: a retrospective observational study
Authors: Xu Hongyan , Ren Qi , Lin Juan , Chen Fang 
Published: February 2023

Abstract
Background: The unplanned interruption during continuous renal replacement therapy (CRRT) can not only decrease the therapeutic outcomes of critically ill patients but also increase the healthcare costs and nurses’ workload. This study aimed to identify the independent factors of unplanned interruption during CRRT to construct a corresponding predictive model to guide clinical practice.
Methods: The clinical data of critically ill patients with CRRT admitted to our hospital (n = 331) were retrospectively analyzed. They were divided into a planned interruption group (n = 238) and an unplanned interruption group (n = 93). Their clinical data such as gender, the blood flow rate, and the filtration fraction were compared between the groups by single-factor analysis. Then, statistically significant variables were analyzed by multivariate analysis to construct a predictive model of unplanned interruption during CRRT, and a receiver operating characteristic (ROC) curve was plotted to validate the model’s predictive value.
Results: The single-factor analysis revealed that gender, chronic diseases, the blood purification catheter function, platelet before CRRT initiation, the blood flow rate, pre-dilution or dialysis, the filtration fraction, and the frequency of blood pump stops in both groups were statistically significant. These eight variables were included in the multivariate analysis showing that chronic diseases (OR: 3.063, 95% CI: 1.200-7.819), the blood purification catheter function (OR: 4.429, 95% CI: 1.270-15.451), the blood flow rate (OR: 0.928, 95% CI: 0.900-0.957), and the frequency of blood pump stops (OR: 1.339, 95% CI: 1.231-1.457) were the independent factors for the unplanned interruption of CRRT. These four factors were used to construct a predictive model and its ROC curve revealed that the area under the curve (AUROC) was 0.952 (95% CI: 0.930-0.973) with a cut-off point of “P = 0.0030” indicating a sensitivity of 0.882 and specificity of 0.899.
Conclusion: Chronic diseases, the blood purification catheter function, the blood flow rate, and the frequency of blood pump stops are independent factors of the unplanned interruption during CRRT. A predictive model constructed using these factors has a certain predictive value for the occurrence of unplanned interruption during CRRT.
A new metaheuristic optimization algorithm based on the participation of smart students to increase the class performance
Authors: Pira Einollah , Rouhi Alireza 
Published: February 2023

Abstract

The learning and teaching power of the students in different courses can be different according to their intelligence and talent. A student may be smart in one course while being lazy in other courses. In order to increase the efficiency of a class, regardless of the class teacher, it is better to teach each course by the smartest student in that course. Inspired by this fact, we present a new meta-heuristic optimization algorithm called Participation of Smart Students (PSS) in increasing the class efficiency. To analyze the effectiveness of the PSS algorithm, we run it on 10 general test functions and 29 test functions from the 2017 IEEE Congress on Evolutionary Computation (CEC 2017). The results of PSS algorithm are compared with the effectiveness of Teaching and Learning-based Optimization (TLBO) Algorithm, Black Widow Optimization (BWO), Political Optimization (PO), Barnacle Mating Optimizer (BMO), Chimpanzee Optimization Algorithm (CHOA), Aquila Optimizer (AO) and City Council Evolution (CCE). Multiple comparison of the results obtained by the Friedman rank test shows that the PSS algorithm has a higher efficiency than the TLBO, BWO, PO, BMO, CHOA, and AO algorithms and almost similar efficiency as the CCE algorithm in terms of finding the closest solution to the optimal one and the hit rate. Moreover, the PSS algorithm has a higher convergence speed than all other algorithms.
Isotopic evidence of photoheterotrophy in Palaeoproterozoic Chlorobi
Authors: Zhang Xiaowen , Paoletti Madeline , Izon Gareth , Fournier Gregory , Summons Roger 
Published: February 2023

Abstract
The reductive tricarboxylic acid (rTCA) cycle is touted as a primordial mode of carbon fixation due to its autocatalytic propensity and oxygen intolerance1,2. Despite this inferred antiquity, however, the earliest rock record affords scant supporting evidence. In fact, based on the chimeric inheritance of rTCA cycle genes within the Chlorobiaceae3, even the utility of the chemical fossil record is now subject to question. While the 1.64-billion-year-old Barney Creek Formation (BCF) contains chemical fossils of the earliest known putative Chlorobiaceae-derived carotenoids, interferences from the accompanying hydrocarbon matrix have hitherto precluded the carbon isotope measurements necessary to establish the physiology of the organisms that produced them. Overcoming this obstacle, here we report a suite of compound-specific carbon isotope measurements identifying a cyanobacterially dominated ecosystem featuring heterotrophic bacteria. Crucially, we demonstrate chlorobactane is 13C-depleted when compared to contemporary equivalents, showing only slight 13C-enrichment over co-existing cyanobacterial carotenoids. These observations demonstrate the late acquisition of autotrophy and the rTCA cycle as predicted via phylogenomic- and molecular-clock-grounded approaches3. We suggest that progressive oxygenation of the Earth System caused an increase in the marine sulfate inventory thereby providing the selective pressure to fuel the Neoproterozoic niche shift toward energy-efficient photoautotrophy within the Chlorobiaceae4.
Dormancy breaking, indirect somatic embryogenesis, and encapsulation of somatic embryos in Black Zira
Authors: Ouzhand Behnaz , Mohayeji Mehdi , Pourseyedi Shahram , Abdolshahi Roohollah 
Published: February 2023

Abstract
Black Zira (Bunium persicum) is an important endemic medicinal plant in Iran. This research was conducted out to assay breaking seed dormancy, develop new protocols for callus induction, somatic embryogenesis, and artificial seed. Seeds were treated with ultrasonic waves in different periods (0, 1, 3, 5, 7, and 9 min). Then, two experiments with IBA (0, 1, 2, 3, and 4 mg L− 1) and a combination of different levels of 2,4-D (0, 1, 2, and 4 mg/l) and BAP (0, 0.5, 1, and 2 mg/l) were designed. Different concentrations of sodium alginate (2, 3, and 4%) and CaCl2 (50, 75, and 100 mM) were prepared for artificial seed production. According to the results, the 7 min ultrasonic waves had the maximum effect on germination (81.87%), while the highest negative effect on germination (2.5%) was reported from 9 min exposure time. In the IBA experiment, the highest percentages of callogenesis, callus weight, and adventitious root were obtained from MS medium containing 4 mg/l IBA. Moreover, 1 mg/l IBA resulted in the highest embryogenesis percentage. In the second experiment, the highest percentage of callus formation and callus weight were achieved in 1 mg/l BAP + 4 mg/l 2,4-D, 0.5 mg/l BAP + 4 mg/l 2,4-D, and 2 mg/l 2,4-D media. The maximum embryogenesis percentage was seen in 0.5 mg/l BAP + 1 mg/l 2,4-D, 0.5 mg/l BAP + 2mg/l 2,4-D, and 1 mg/l 2,4-D media. The highest germination rate (77.77%) was obtained from encapsulated embryos with 3% sodium alginate and 100 mM CaCl2.
A novel dammarane triterpenoid alleviates atherosclerosis by activating the LXRα pathway
Authors: Huang Yan , Ran Xiaodong , Liu Hongmei , Luo Mingming , Qin Yiyu , Yan Jinqiong , Li Xiaohui , Jia Yi 
Published: February 2023

Abstract
Background
We have previously demonstrated that ginsenoside compound K can attenuate the formation of atherosclerotic lesions. Therefore, ginsenoside compound K has potential for atherosclerosis therapy. How to improve the druggability and enhance the antiatherosclerotic activity of ginsenoside compound K are the core problems in the prevention and treatment of atherosclerosis. CKN is a ginsenoside compound K derivative that was previously reported to have excellent antiatherosclerotic activity in vitro, and we have applied for international patents for it.
Methods
Male C57BL/6 ApoE-/- mice were fed a high-fat and high-choline diet to induce atherosclerosis and were subjected to in vivo studies. In vitro, the CCK-8 method was applied to evaluate cytotoxicity in macrophages. Foam cells were utilized, and cellular lipid determination was performed for in vitro studies. The area of atherosclerotic plaque and fatty infiltration of the liver were measured by image analysis. Serum lipid and liver function were determined by a seralyzer. Immunofluorescence and western blot analysis were conducted to explore the alterations in the expression levels of lipid efflux-related proteins. Molecular docking, reporter gene experiments and cellular thermal shift assays were used to verify the interaction between CKN and LXRα.
Results
After confirming the therapeutic effects of CKN, molecular docking, reporter gene experiments and cellular thermal shift assays were used to predict and investigate the antiatherosclerotic mechanisms of CKN. CKN exhibited the greatest potency, with a 60.9% and 48.1% reduction in en face atherosclerotic lesions on the thoracic aorta and brachiocephalic trunk, reduced plasma lipid levels and decreased foam cell levels in the vascular plaque content in HHD-fed ApoE−/− mice. Moreover, CKN in the present study may exert its antiatherosclerotic effects through activated ABCA1 by promoting LXRα nuclear translocation and reducing the adverse effects of LXRα activation.
Conclusions
Our results revealed that CKN prevented the formation of atherosclerosis in ApoE−/− mice by activating the LXRα pathway.
Size and timing of giant Chilean earthquake controlled by rock composition and forearc structure
Authors: Julve Joaquín , Barbot Sylvain , Moreno Marcos , Tassara Andrés , Araya Rodolfo , Catalán Nicole , Crempien Jorge , Becerra Valeria 
Published: February 2023

Abstract
In 1960, the Southern Chile subduction zone produced the Mw9.5 Valdivia mega-quake, the largest earthquake on record, breaking up the entire seismogenic zone for 1,000 km along the Chile Trench. Crustal deformation in the aftermath involved a combination of fault motion and viscoelastic flow. The region has recovered its seismic potential, as evidenced by the 2016 Mw7.6 Melinka earthquake that only partially affected the 1960 rupture zone below the continental shelf. The succession of such partial and full ruptures at subduction megathrusts controls seismic hazards but remains poorly understood. Here we show how the distribution of frictional and rheological properties within the forearc and the surrounding lithosphere, controls the size and timing of great and giant earthquakes at the Chile Trench. We build numerical simulations of the seismic cycle in Southern Chile using frictional and viscoelastic properties calibrated to geologic, geodetic, and geophysical information. The model explains the recurrence times of great and giant earthquakes from paleoseismic data, but also the fault slip distribution and crustal deformation associated with the Melinka and Valdivia earthquakes. Our synoptic model illuminates the structural and geological controls on seismicity, explaining the typical sequence of blind and trench-breaking ruptures at subduction zones.
A new combined approach of two neural-metaheuristic techniques based on the Cuckoo optimization algorithm and backtracking search algorithms for predicting and appraisal of landslide susceptibility mapping
Authors: Moayedi Hossein , Dehrashid Atefeh Ahmadi 
Published: February 2023

Abstract
In this research, a hybrid Backtracking Search Algorithm (BSA) and Cuckoo Optimization Algorithm (COA)-based artificial neural network (ANN) model (BSA-MLP and COA-MLP) was used to predict landslide susceptibility mapping (LSM) in an area in the province of Kurdistan, west of Iran. The input dataset includes elevation, slope angle, rainfall, and land use. The output is a value that shows how likely a landslide will happen. The parameters and weights of the BSA and COA algorithms were fine-tuned to produce the most accurate LSM. Table 2 illustrates the effect of the number of layers and neurons on the accuracy of models produced using the standard ANN approach. Root-mean-squared error (RMSE) and correlation coefficient (R2) were used to compare different network designs. Table 2 shows that feed-forward back-propagation with six hidden layers (a transit function and six neurons in the hidden layer) gave the best results. The model got more accurate as the number of hidden layers and neurons increased, but it stopped getting better when there were four hidden layers. After a certain point, the model became too complicated to be worth the benefit of being more accurate. A model may be overfitted if it employs several hidden layers and neurons. The results of the first optimization stage serve as the basis for further operations. The succeeding parts thus use the findings of these networks. In the next step, to make the models better, the number of neurons in each hidden layer will be changed. This suggests that the number of neurons may increase or decrease depending on the results of the first phase. Many networks with different types of neurons and combinations of layers have been built to find the best architecture. The most successful network design was generated via a feed-forward back-propagation algorithm with six hidden layers. A model may be overfitting if it has several hidden layers and neurons.
CUTS: A Fully Unsupervised Framework for Medical Image Segmentation
Authors: Liu Chen , Amodio Matthew , Shen Liangbo , Gao Feng , Avesta Arman , Aneja Sanjay , Wang Jay , Priore Lucian Del , Krishnaswamy Smita 
Published: February 2023

Abstract
In this work we introduce CUTS (Contrastive and Unsupervised Training for Segmentation), the first fully unsupervised deep learning framework for medical image segmentation to better utilize the vast majority of imaging data that is not labeled or annotated. Segmenting medical images into regions of interest is a critical task for facilitating both patient diagnoses and quantitative research. A major limiting factor is the lack of labeled data, as obtaining expert annotations for each new set of imaging data or task can be expensive, labor intensive, and inconsistent across annotators. Thus, we utilize self-supervision from pixels and their local neighborhoods in the images themselves. Our unsupervised approach optimizes a training objective that leverages concepts from contrastive learning and autoencoding. Previous contrastive learning approaches either focused on image-level contrastive training and therefore lacked sufficient patch-level information necessary for segmentation or framed themselves as pre-training steps that require further supervised fine-tuning. In contrast, our framework segments medical images with a novel two-stage approach without relying on any labeled data at any stage. The first stage involves the creation of a "pixel-centered patch" that embeds every pixel along with its surrounding patch, using a vector representation in a high-dimensional latent embedding space. The second stage utilizes diffusion condensation, a multi-scale topological data analysis approach, to dynamically coarse-grain these embedding vectors at all levels of granularity. The final outcome is a series of coarse-to-fine segmentations that highlight image structures at various scales. In this work, we show successful multi-scale segmentation on natural images, retinal fundus images, and brain MRI images. Our framework delineates structures and patterns at different scales which, in the cases of medical images, may carry distinct information relevant to clinical interpretation. Quantitatively, our framework demonstrates beyond 100% improvement on dice coefficient and Hausdorff distance compared to existing unsupervised methods on geographic atrophy segmentation in retinal fundus images. When segmenting ventricles in the brain MRI images, our framework outperforms existing unsupervised methods by a factor between 2% to 300% on dice coefficient and between 14% and 77% on Hausdorff distance. As we tackle the problem of segmenting medical images at multiple meaningful granularities without relying on any label, we hope to demonstrate the possibility to circumvent tedious and repetitive manual annotations in future practice.
Transcranial Random Noise Stimulation combined with Cognitive Training for Treating ADHD: A Randomized, Sham-Controlled Clinical Trial
Authors: Nahum Mor , Dakwar-Kawar Ornella , Mairon Noam , Hochman Shachar , Berger Itai , Cohen-Kadosh R 
Published: February 2023

Abstract
Non-invasive brain stimulation has been suggested as a potential treatment for improving symptomology and cognitive deficits in Attention-Deficit/Hyperactivity Disorder (ADHD), the most common childhood neurodevelopmental disorder. Here, we examined whether a novel form of stimulation, high-frequency transcranial random noise stimulation (tRNS), applied with cognitive training (CT), may impact symptoms and neural oscillations in children with ADHD. We conducted a randomized, double-blind sham-controlled trial in 23 unmedicated children with ADHD, who received either tRNS over the right inferior frontal gyrus (rIFG) and left dorsolateral prefrontal cortex (lDLPFC) or sham stimulation for 2 weeks, combined with CT. tRNS + CT yielded significant clinical improvements (reduced ADHD rating-scale scores) following treatment, compared to the control intervention. These improvements were maintained at a 3-week follow-up. Moreover, resting state (RS)-EEG periodic beta bandwidth of the extracted peaks was reduced in the experimental compared to control group immediately following treatment, with further reduction at follow-up. A marginally lower aperiodic exponent which reflects a higher excitation/inhibition was seen in the experimental compared to control group. The experimental group further exhibited longer sleep onset latencies and more wake-up times following treatment compared to the control group. No significant group differences were seen in executive functions, nor in reported adverse events. We conclude that tRNS + CT has a lasting clinical effect on ADHD symptoms and on beta activity. These results provide a promising direction towards a novel intervention in paediatric ADHD that induces sustained effects.